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Phenol and some of its derivatives exhibit interesting tunneling motions consisting of two groups of transitions separated by a few hundred MHz. Recently, one of its derivatives, 2,6-di-tert-butylphenol, has shown additional hyperfine tunneling components, the origin of which remains unclear. In this work, another member of the family, 2,6-diethylphenol, has been investigated through its rotational spectrum. The jet-cooled broadband chirped-pulse Fourier transform microwave spectra in the 2-8 GHz frequency region revealed the presence of two conformers. The comparison with the equilibrium structure obtained by computational calculations at the B3LYP-D3(BJ)/Def2-TZVP level validates the structural determination and the orientation of the lateral ethyl groups. Additional observation of all the singly-substituted 13C isotopologues for the most stable ones allowed the determination of the substitution structure by means of the Kraitchman equations. Both conformers exhibited tunneling that was reproduced using an advanced 1D model, which provides an estimate of the barrier height for both conformers.

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A 67-year-old man visited our hospital complaining of dark-colored urine and upper abdominal pain. Magnetic resonance cholangiopancreatography showed stricture of the distal bile duct, and contrast-enhanced computed tomography showed irregular thickening of the distal bile duct wall. However, no enlarged lymph nodes, pancreatic tumors, or other neoplastic lesions were apparent around the bile duct. Endoscopic ultrasonography and intraductal ultrasonography showed irregular thickening of the inner hypoechoic layer without the disappearance of the innermost thin hyperechoic layer. On the basis of these findings, we considered that the bile duct lesion was of non-epithelial origin. Thus, we repeatedly performed bile duct biopsies from the same site under fluoroscopy to obtain a sample of the submucosal tissue. The pathological diagnosis was diffuse large B-cell lymphoma, and the patient received systemic chemotherapy (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone). After six courses of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone, positron emission tomography-computed tomography showed the disappearance of 18-fluorodeoxyglucose uptake in the bile duct and endoscopic retrograde cholangiography showed improvement of the bile duct stricture. Endoscopic findings and repeated biopsies were useful in making the diagnosis of primary biliary diffuse large B-cell lymphoma.

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Numerous nanoparticles have been utilized to deliver Fe2+ for tumor ferroptosis therapy, which can be readily converted to Fe3+via Fenton reactions to generate hydroxyl radical (•OH). However, the ferroptosis therapeutic efficacy of large tumors is limited due to the slow conversion of Fe3+ to Fe2+via Fenton reactions. Herein, a strategy of intratumor Fe3+/2+ cyclic catalysis is proposed for ferroptosis therapy of large tumors, which was realized based on our newly developed hollow mesoporous iron sesquioxide nanoparticle (HMISN). Cisplatin (CDDP) and Gd-poly(acrylic acid) macrochelates (GP) were loaded into the hollow core of HMISN, whose surface was modified by laccase (LAC). Fe3+, CDDP, GP, and LAC can be gradually released from CDDP@GP@HMISN@LAC in the acidic tumor microenvironment. The intratumor O2 can be catalyzed into superoxide anion (O2•-) by LAC, and the intratumor NADPH oxidases can be activated by CDDP to generate O2•-. The O2•- can react with Fe3+ to generate Fe2+, and raise H2O2 level via the superoxide dismutase. The generated Fe2+ and H2O2 can be fast converted into Fe3+ and •OH via Fenton reactions. The cyclic catalysis of intratumor Fe3+/2+ initiated by CDDP@GP@HMISN@LAC can be used for ferroptosis therapy of large tumors.

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Background: The application of rituximab has significantly enhanced the overall survival rates in patients with diffuse large B-cell lymphoma (DLBCL). Regrettably, a significant number of patients still progress to relapse/refractory DLBCL (rrDLBCL). Methods: Herein, we employed targeted sequencing of 55 genes to investigate if gene mutations could predict the progression to rrDLBCL. Additionally, we compared the mutation profiles at the time of DLBCL diagnosis with those found in rrDLBCL cases. Results: Our findings highlighted significantly elevated mutation frequencies of TP53, MEF2B and CD58 in diagnostic biopsies from patients who progressed to relapse or refractory disease, with CD58 mutations exclusively observed in the rrDLBCL group. In assessing the predictive power of mutation profiles for treatment responses in primary DLBCL patients, we found that the frequency of CARD11 mutations was substantially higher in non-response group as compared with those who responded to immunochemotherapy. In addition, we revealed mutations in HIST2H2AB, BCL2, NRXN3, FOXO1, HIST1H1C, LYN and TBL1XR1 genes were only detected in initial diagnostic biopsies, mutations in the EBF1 gene were solely detected in the rrDLBCL patients. Conclusion: Collectively, this study elucidates some of the genetic mechanisms contributing to the progression of rrDLBCL and suggests that the presence of CD58 mutations might serve as a powerful predictive marker for relapse/refractory outcomes in primary DLBCL patients.

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Large-scale studies are essential to answer questions about complex microbial communities that can be extremely dynamic across hosts, environments, and time points. However, managing acquisition, processing, and analysis of large numbers of samples poses many challenges, with cross-contamination being the biggest obstacle. Contamination complicates analysis and results in sample loss, leading to higher costs and constraints on mixed sample type study designs. While many researchers opt for 96-well plates for their workflows, these plates present a significant issue: the shared seal and weak separation between wells leads to well-to-well contamination. To address this concern, we propose an innovative high-throughput approach, termed as the Matrix method, which employs barcoded Matrix Tubes for sample acquisition. This method is complemented by a paired nucleic acid and metabolite extraction, utilizing 95% (vol/vol) ethanol to stabilize microbial communities and as a solvent for extracting metabolites. Comparative analysis between conventional 96-well plate extractions and the Matrix method, measuring 16S rRNA gene levels via quantitative polymerase chain reaction, demonstrates a notable decrease in well-to-well contamination with the Matrix method. Metagenomics, 16S rRNA gene amplicon sequencing (16S), and untargeted metabolomics analysis via liquid chromatography-tandem mass spectrometry (LC-MS/MS) confirmed that the Matrix method recovers reproducible microbial and metabolite compositions that can distinguish between subjects. This advancement is critical for large-scale study design as it minimizes well-to-well contamination and technical variation, shortens processing times, and integrates with automated infrastructure for enhancing sample randomization and metadata generation. IMPORTANCE: Understanding dynamic microbial communities typically requires large-scale studies. However, handling large numbers of samples introduces many challenges, with cross-contamination being a major issue. It not only complicates analysis but also leads to sample loss and increased costs and restricts diverse study designs. The prevalent use of 96-well plates for nucleic acid and metabolite extractions exacerbates this problem due to their wells having little separation and being connected by a single plate seal. To address this, we propose a new strategy using barcoded Matrix Tubes, showing a significant reduction in cross-contamination compared to conventional plate-based approaches. Additionally, this method facilitates the extraction of both nucleic acids and metabolites from a single tubed sample, eliminating the need to collect separate aliquots for each extraction. This innovation improves large-scale study design by shortening processing times, simplifying analysis, facilitating metadata curation, and producing more reliable results.

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The assessment of social determinants of health (SDoH) within healthcare systems is crucial for comprehensive patient care and addressing health disparities. Current challenges arise from the limited inclusion of structured SDoH information within electronic health record (EHR) systems, often due to the lack of standardized diagnosis codes. This study delves into the transformative potential of large language models (LLM) to overcome these challenges. LLM-based classifiers-using Bidirectional Encoder Representations from Transformers (BERT) and A Robustly Optimized BERT Pretraining Approach (RoBERTa)-were developed for SDoH concepts, including homelessness, food insecurity, and domestic violence, using synthetic training datasets generated by generative pre-trained transformers combined with authentic clinical notes. Models were then validated on separate datasets: Medical Information Mart for Intensive Care-III and our institutional EHR data. When training the model with a combination of synthetic and authentic notes, validation on our institutional dataset yielded an area under the receiver operating characteristics curve of 0.78 for detecting homelessness, 0.72 for detecting food insecurity, and 0.83 for detecting domestic violence. This study underscores the potential of LLMs in extracting SDoH information from clinical text. Automated detection of SDoH may be instrumental for healthcare providers in identifying at-risk patients, guiding targeted interventions, and contributing to population health initiatives aimed at mitigating disparities.

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OBJECTIVE: This study aimed to investigate the performance of generative pre-trained transformer-4 (GPT-4) on the Certification Test for Mental Health Management and whether tuned prompts could improve its performance. METHODS: This study used a 3 × 2 factorial design to examine the performance according to test difficulty (courses) and prompt conditions. We prepared 200 multiple-choice questions (600 questions overall) for each course using the Certification Test for Mental Health Management (levels I-III) and essay questions from the level I test for the previous four examinations. Two conditions were used: a simple prompt condition using the questions as prompts and tuned prompt condition using techniques to obtain better answers. GPT-4 (gpt-4-0613) was adopted and implemented using the OpenAI API. RESULTS: The simple prompt condition scores were 74.5, 71.5, and 64.0 for levels III, II, and I, respectively. The tuned and simple prompt condition scores had no significant differences (OR = 1.03, 95% CI; 0.65-1.62, p = 0.908). Incorrect answers were observed in the simple prompt condition because of the inability to make choices, whereas no incorrect answers were observed in the tuned prompt condition. The average score for the essay questions under the simple prompt condition was 22.5 out of 50 points (45.0%). CONCLUSION: GPT-4 had a sufficient knowledge network for occupational mental health, surpassing the criteria for levels II and III tests. For the level I test, which required the ability to describe more advanced knowledge accurately, GPT-4 did not meet the criteria. External information may be needed when using GPT-4 at this level. Although the tuned prompts did not significantly improve the performance, they were promising in avoiding unintended outputs and organizing output formats. UMIN trial registration: UMIN-CTR ID = UMIN000053582.

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Understanding how large carnivores utilize space is crucial for management planning in human-dominated landscape and enhances the accuracy of population size estimates. However, Eurasian lynx display a large inter-population variation in the size of home ranges across their European range which makes extrapolation to broader areas of a species distribution problematic. This study evaluates variations in home range size for 35 Eurasian lynx in the Western Carpathians during 2011-2022 based on GPS telemetry and explains how intrinsic and environmental factors shape lynx spatial behaviour when facing anthropogenic pressure. The average annual home range size of lynx ranged from 283 (± 42 SE) to 360 (± 60 SE) km2 for males and from 148 (± 50 SE) to 190 (± 70 SE) km2 for females, depending on home range estimator (95% MCP, KDE and AKDE). Females with kittens had smaller annual and summer home ranges compared to non-reproducing females and subadults had smaller home ranges compared to adults. Lynx home range size was explained by availability of roe deer, except for summer, when alternative prey was likely available. We also found clear evidence of human-induced changes in lynx home range size, in particular, forest cover significantly decreased the home range size of male lynx during summer while road density led to an expansion of both annual and summer lynx home ranges. Lynx exhibited consistent fidelity to their home ranges throughout consecutive seasons, showing no seasonal variations. Strong territoriality was observed among competing males maintaining relatively low home range overlaps and considerable distances between centres of activity. The most pronounced tendency for association was observed between males and females, maintaining relatively close proximity year-round. The insights into lynx spatial requirements provided by our study will greatly enhance the accuracy of population size estimates and effectiveness of mitigation measures across the Western Carpathians.

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PURPOSE: Obstructive sleep apnea (OSA) is a heterogeneous disorder requiring personalized diagnostic approaches. Restless sleep and excessive daytime sleepiness (EDS) frequently accompany OSA, and are mainly linked to sleep fragmentation secondary to apneas and/or hypopneas. In this study, we aimed to analyze the characteristics of LMMs in OSA and to evaluate interrelationship between LMMs and EDS. METHODS: Untreated-naïve adult OSA patients, with vs. without EDS were prospectively enrolled. Patients with comorbid neurological/psychiatric diagnosis, usage of drugs/substances known to affect sleep and positive airway pressure therapy were excluded. Routine evaluation of video-polysomnography was followed by LMM scoring. LMMs were compared between OSA with vs. without EDS, and correlations of LMMs with ESS scores and macrostructural sleep parameters were analyzed. RESULTS: Sixty patients were included (median age 43.5 [37.0] years, %78.3 men); 17 had EDS with Epworth Sleepiness Scale (ESS) ≥ 10 (28.3%). Total LMM index in total sleep time (TST) was 7.9 [20.6]. Total LMM index in TST (p = 0.048) and N1 (p = 0.020), and arousal-related LMM index in TST (p = 0.050) and N1 (p = 0.026) were higher in OSA with EDS than those without EDS. ESS scores were positively correlated with total (r = 0.332,p = 0.028) and arousal-related (r = 0.338,p = 0.025) LMM indexes in N1, and abnormal respiratory event-related LMM indexes in N1 (r = 0.440,p = 0.003) and N3 (r = 0.293,p = 0.050) after correction for age, sex, body-mass-index and apnea-hypopnea index. CONCLUSION: Our study demonstrated that LMMs were more frequent in OSA with EDS than those without EDS. This may have broad implications for the mechanisms of motor restlessness and residual sleepiness in OSA and warrants larger-scale, long-term follow-up studies. CLINICAL TRIAL REGISTRATION: No clinical trial registration due to the observational design of the study.

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OBJECTIVE: Assessment of the readability, accuracy, quality, and completeness of ChatGPT (Open AI, San Francisco, CA), Gemini (Google, Mountain View, CA), and Claude (Anthropic, San Francisco, CA) responses to common questions about rhinoplasty. METHODS: Ten questions commonly encountered in the senior author's (SPM) rhinoplasty practice were presented to ChatGPT-4, Gemini and Claude. Seven Facial Plastic and Reconstructive Surgeons with experience in rhinoplasty were asked to evaluate these responses for accuracy, quality, completeness, relevance, and use of medical jargon on a Likert scale. The responses were also evaluated using several readability indices. RESULTS: ChatGPT achieved significantly higher evaluator scores for accuracy, and overall quality but scored significantly lower on completeness compared to Gemini and Claude. All three chatbot responses to the ten questions were rated as neutral to incomplete. All three chatbots were found to use medical jargon and scored at a college reading level for readability scores. CONCLUSIONS: Rhinoplasty surgeons should be aware that the medical information found on chatbot platforms is incomplete and still needs to be scrutinized for accuracy. However, the technology does have potential for use in healthcare education by training it on evidence-based recommendations and improving readability. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

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PURPOSE: This study aimed to developed a novel and practical method to quantify the involvement of lesion in osteonecrosis of the femoral head (ONFH). We hypothesized that the new metric large lesion ratio (LLR) had promising prognostic value. METHODS: A total of 131 hips with non-traumatic ONFH were included in this retrospective study. Patient aged 18-60 with MRI-confirmed diagnosis, and a minimum of 2-year follow-up or radiographic collapse progression during follow-up were included. Patients with prior hip surgery, incomplete data or advanced ONFH at baseline (femoral head collapse > 2 mm or osteoarthritis) were excluded. Involvement of necrotic lesion was evaluated by calculating LLR. The differences of LLR between collapse progression and non-progression groups were investigated, and the differences among different scanning parameters groups were also examined. Prognostic value of LLR was examined by multivariate regression analysis. Receiver operating characteristic curves (ROC) were constructed and areas under the curve (AUC) were compared. RESULTS: The median of LLR was 66.67% in the collapse progression group, which was significantly higher compared with 25.00% in the non-progression group (P < 0.001). Subgroups analysis showed that LLR were significantly higher in the collapse progression group of Japanese Investigation Committee type C1 (P < 0.001)and C2 (P = 0.002). Multivariate regression showed that LLR were independently correlated with collapse progression (OR, 1.46 [95% CI, 1.24-1.78]; P < 0.001). ROC analysis showed that the AUC for LLR was 0.84, outperforming the 0.74 AUC OF the JIC classification. CONCLUSION: LLR could served as a efficient tool to assess the risk of collapse progression and guide the selection of treatment strategy.

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BACKGROUNDS: The use of large language models (LLMs) in medicine can help physicians improve the quality and effectiveness of health care by increasing the efficiency of medical information management, patient care, medical research, and clinical decision-making. METHODS: We collected 34 frequently asked questions about glucocorticoid-induced osteoporosis (GIOP), covering topics related to the disease's clinical manifestations, pathogenesis, diagnosis, treatment, prevention, and risk factors. We also generated 25 questions based on the 2022 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis (2022 ACR-GIOP Guideline). Each question was posed to the LLM (ChatGPT-3.5, ChatGPT-4, and Google Gemini), and three senior orthopedic surgeons independently rated the responses generated by the LLMs. Three senior orthopedic surgeons independently rated the answers based on responses ranging between 1 and 4 points. A total score (TS) > 9 indicated 'good' responses, 6 ≤ TS ≤ 9 indicated 'moderate' responses, and TS < 6 indicated 'poor' responses. RESULTS: In response to the general questions related to GIOP and the 2022 ACR-GIOP Guidelines, Google Gemini provided more concise answers than the other LLMs. In terms of pathogenesis, ChatGPT-4 had significantly higher total scores (TSs) than ChatGPT-3.5. The TSs for answering questions related to the 2022 ACR-GIOP Guideline by ChatGPT-4 were significantly higher than those for Google Gemini. ChatGPT-3.5 and ChatGPT-4 had significantly higher self-corrected TSs than pre-corrected TSs, while Google Gemini self-corrected for responses that were not significantly different than before. CONCLUSIONS: Our study showed that Google Gemini provides more concise and intuitive responses than ChatGPT-3.5 and ChatGPT-4. ChatGPT-4 performed significantly better than ChatGPT3.5 and Google Gemini in terms of answering general questions about GIOP and the 2022 ACR-GIOP Guidelines. ChatGPT3.5 and ChatGPT-4 self-corrected better than Google Gemini.

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OBJECTIVE: The reading level of health educational materials significantly influences the understandability and accessibility of the information, particularly for minoritized populations. Many patient educational resources surpass widely accepted standards for reading level and complexity. There is a critical need for high-performing text simplification models for health information to enhance dissemination and literacy. This need is particularly acute in cancer education, where effective prevention and screening education can substantially reduce morbidity and mortality. METHODS: We introduce Simplified Digestive Cancer (SimpleDC), a parallel corpus of cancer education materials tailored for health text simplification research, comprising educational content from the American Cancer Society, Centers for Disease Control and Prevention, and National Cancer Institute. The corpus includes 31 web pages with the corresponding manually simplified versions. It consists of 1183 annotated sentence pairs (361 train, 294 development, and 528 test). Utilizing SimpleDC and the existing Med-EASi corpus, we explore Large Language Model (LLM)-based simplification methods, including fine-tuning, reinforcement learning (RL), reinforcement learning with human feedback (RLHF), domain adaptation, and prompt-based approaches. Our experimentation encompasses Llama 2, Llama 3, and GPT-4. We introduce a novel RLHF reward function featuring a lightweight model adept at distinguishing between original and simplified texts when enables training on unlabeled data. RESULTS: Fine-tuned Llama models demonstrated high performance across various metrics. Our RLHF reward function outperformed existing RL text simplification reward functions. The results underscore that RL/RLHF can achieve performance comparable to fine-tuning and improve the performance of fine-tuned models. Additionally, these methods effectively adapt out-of-domain text simplification models to a target domain. The best-performing RL-enhanced Llama models outperformed GPT-4 in both automatic metrics and manual evaluation by subject matter experts. CONCLUSION: The newly developed SimpleDC corpus will serve as a valuable asset to the research community, particularly in patient education simplification. The RL/RLHF methodologies presented herein enable effective training of simplification models on unlabeled text and the utilization of out-of-domain simplification corpora.

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Of all cutaneous lymphomas, 25% are primary cutaneous B-cell lymphomas (PCBCLs). Of these, primary cutaneous follicle center lymphoma (PCFCL), primary cutaneous marginal zone B-cell lymphoma (PCMZL), and primary cutaneous diffuse large B-cell lymphoma, leg type (PCDLBCL-LT) are the most common subtypes. For the diagnosis of PCBCLs, a biopsy combined with immunohistochemistry and histological examination is the gold standard. PCBCLs are categorized into indolent or intermediate to aggressive subtypes based on their clinical behavior in a clinically oriented approach. PCDLBCL-LT has an aggressive course that spreads to extracutaneous sites in about 45% of cases, whereas PCFCL and PCMZL are indolent diseases. As a result, instrumental staging is advised for PCDLBCL-LT but not for extracutaneous disease after a diagnosis of PCMZL or PCFCL. Lastly, dermatoscopy may offer a novel diagnostic tool to improve the clinical recognition of various PCBCL subtypes when used in conjunction with a strong clinical suspicion.

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Background: The identification of compound-protein interactions (CPIs) is crucial for drug discovery and understanding mechanisms of action. Accurate CPI prediction can elucidate drug-target-disease interactions, aiding in the discovery of candidate compounds and effective synergistic drugs, particularly from traditional Chinese medicine (TCM). Existing in silico methods face challenges in prediction accuracy and generalization due to compound and target diversity and the lack of largescale interaction datasets and negative datasets for model learning. Methods: To address these issues, we developed a computational model for CPI prediction by integrating the constructed large-scale bioactivity benchmark dataset with a deep learning (DL) algorithm. To verify the accuracy of our CPI model, we applied it to predict the targets of compounds in TCM. An herb pair of Astragalus membranaceus and Hedyotis diffusaas was used as a model, and the active compounds in this herb pair were collected from various public databases and the literature. The complete targets of these active compounds were predicted by the CPI model, resulting in an expanded target dataset. This dataset was next used for the prediction of synergistic antitumor compound combinations. The predicted multi-compound combinations were subsequently examined through in vitro cellular experiments. Results: Our CPI model demonstrated superior performance over other machine learning models, achieving an area under the Receiver Operating Characteristic curve (AUROC) of 0.98, an area under the precision-recall curve (AUPR) of 0.98, and an accuracy (ACC) of 93.31% on the test set. The model's generalization capability and applicability were further confirmed using external databases. Utilizing this model, we predicted the targets of compounds in the herb pair of Astragalus membranaceus and Hedyotis diffusaas, yielding an expanded target dataset. Then, we integrated this expanded target dataset to predict effective drug combinations using our drug synergy prediction model DeepMDS. Experimental assay on breast cancer cell line MDA-MB-231 proved the efficacy of the best predicted multi-compound combinations: Combination I (Epicatechin, Ursolic acid, Quercetin, Aesculetin and Astragaloside IV) exhibited a half-maximal inhibitory concentration (IC50) value of 19.41 µM, and a combination index (CI) value of 0.682; and Combination II (Epicatechin, Ursolic acid, Quercetin, Vanillic acid and Astragaloside IV) displayed a IC50 value of 23.83 µM and a CI value of 0.805. These results validated the ability of our model to make accurate predictions for novel CPI data outside the training dataset and evaluated the reliability of the predictions, showing good applicability potential in drug discovery and in the elucidation of the bioactive compounds in TCM. Conclusion: Our CPI prediction model can serve as a useful tool for accurately identifying potential CPI for a wide range of proteins, and is expected to facilitate drug research, repurposing and support the understanding of TCM.

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Larimichthys crocea is an important economic fish of East Asia, and numerous studies have been conducted on its breeding, aquaculture, preservation and processing; however, there is no systematic review of the literature on the research of Larimichthys crocea. Derwent Data Analyzer (DDA) was used to analyze 1192 Larimichthys crocea research papers indexed by SCI-E, CSCD and KCI from 2001 to 2023. The number of research publications on Larimichthys crocea has rapidly increased, and institutions and scholars from China, the United States, South Korea, Japan, and Norway have conducted the majority of Larimichthys crocea research. The immune response, Pseudomonas plecoglossicida, gene expression, lipid immune response, transcriptomics and other areas have attracted the most attention. To increase the immunity and disease resistance of Larimichthys crocea and improve its survival, growth, storage and transport, researchers have carried out a large amount of research, which has promoted not only the culture of Larimichthys crocea but also the restoration of wild Larimichthys crocea and the rehabilitation of the ecological environment.

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The elasmobranch population is declining in the Bay of Bengal of Bangladesh due to large-mesh gill net fishing, locally known as the Lakkha net, which primarily targets Indian threadfin (Leptomelanosoma indicum). This study was the first attempt to identify megafaunal bycatch in Lakkha fishing and assess its vulnerability using Productivity Susceptibility Analysis. A total of 40 elasmobranch bycatch species were identified, with sharks comprising 13 species from three families, while 27 rays belonged to six families, with the majority belonging to the Myliobatiformes order (60 %). Productivity and susceptibility scores were assigned to all identified species, with values ranging from 1.27 to 2.73 and 1.50 to 2.63, respectively. The target Lakkha fish exhibited the highest susceptibility score, followed by several pelagic sharks and eagle rays. Vulnerability assessment revealed that 31.7 % (n = 13) of species were highly vulnerable, while 43.9 % (n = 18) were classified as moderate, and 24.4 % (n = 10) were considered to have low vulnerability. All the high-risk megafauna species (n = 13) are classified as threatened by the global IUCN Red List. Sensitivity analysis highlighted susceptibility as a major contributor to species' vulnerability. Alterations in susceptibility scores led to significant changes in the vulnerability status of many species. The overall data quality assessment indicated moderate data quality across species, with variability observed between productivity (76 % of species received a poor data quality score) and susceptibility attributes. However, vulnerability of these species can be reduced through adequate gear modification, shorter net deployment periods, adoption of safe discharge techniques, identification of critical habitats, and establishment of marine protected areas within this region. This study provides valuable insights into the species composition and vulnerability of elasmobranchs in the Lakkha gill net fishery, emphasizing the need for conservation measures to mitigate bycatch impacts on threatened species.

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Machine perfused ex-vivo organs offer an excellent experimental platform, e.g., for studying organ physiology and for conducting pre-clinical trials for drug delivery. One main challenge in machine perfusion is the accurate assessment of organ condition. Assessment is often performed using viability markers, i.e., lactate concentrations and blood gas analysis. Nonetheless, existing markers for condition assessment can be inconclusive, and novel assessment methods remain of interest. Over the last decades, several imaging modalities have given unique insights into the assessment of organ condition. A systematic review was conducted according to accepted guidelines to evaluate these medical imaging methods, focussed on literature that use machine perfused human-sized organs, that determine organ condition with medical imaging. A total of 18 out of 1,465 studies were included that reported organ condition results in perfused hearts, kidneys, and livers, using both conventional viability markers and medical imaging. Laser speckle imaging, ultrasound, computed tomography, and magnetic resonance imaging were used to identify local ischemic regions and quantify intra-organ perfusion. A detailed investigation of metabolic activity was achieved using 31P magnetic resonance imaging and near-infrared spectroscopy. The current review shows that medical imaging is a powerful tool to assess organ condition.

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Commonsense reasoning has emerged as a challenging problem in Artificial Intelligence (AI). However, one area of commonsense reasoning that has not received nearly as much attention in the AI research community is plausibility assessment, which focuses on determining the likelihood of commonsense statements. Human-annotated benchmarks are essential for advancing research in this nascent area, as they enable researchers to develop and evaluate AI models effectively. Because plausibility is a subjective concept, it is important to obtain nuanced annotations, rather than a binary label of 'plausible' or 'implausible'. Furthermore, it is also important to obtain multiple human annotations for a given statement, to ensure validity of the labels. In this data article, we describe the process of re-annotating an existing commonsense plausibility dataset (SemEval-2020 Task 4) using large-scale crowdsourcing on the Amazon Mechanical Turk platform. We obtain 10,000 unique annotations on a corpus of 2000 sentences (five independent annotations per sentence). Based on these labels, each was labelled as plausible, implausible, or ambiguous. Next, we prompted the GPT-3.5 and GPT-4 models developed by OpenAI. Sentences from the human-annotated files were fed into the models using custom prompt templates, and the models' generated labels were used to determine if they were aligned with those output by humans. The PMC-Dataset is meant to serve as a rich resource for analysing and comparing human and machine commonsense reasoning capabilities, specifically on plausibility. Researchers can utilise this dataset to train, fine-tune, and evaluate AI models on plausibility. Applications include: determining the likelihood of everyday events, assessing the realism of hypothetical scenarios, and distinguishing between plausible and implausible statements in commonsense text. Ultimately, we intend for the dataset to support ongoing AI research by offering a robust foundation for developing models that are better aligned with human commonsense reasoning.

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OBJECTIVE: To explore the factors affecting the prognosis of patients with acute posterior circulation large vessel occlusion cerebral infarction (PCO) after mechanical thrombectomy. METHOD: A retrospective study was conducted on a total of 58 patients who received thrombectomy and presented within 24 h of onset with PCO from 31 September 2020 to 31 December 2022. They were divided into two groups based on a 90-day mRS score(The mRS score of 0-3 was defined as a good prognosis, and 4-6 was defined as a poor prognosis).A univariate analysis was conducted on baseline data such as age and patient past medical history, as well as extended cerebral infarction thrombolysis grade (eTICI grade) and incidence of symptomatic intracranial hemorrhage (sICH) after surgery, for the groups with good prognosis and poor prognosis. Factors affecting the 90-day prognosis of patients were also analyzed in subgroups. RESULTS: The preoperative National Institutes of Health Stroke Scale (NIHSS score)[21(12-35) vs 35(35-35)], postoperative 24-h NIHSS score[13(8-22) vs 35(35-35)], computed tomography (CT)[9(9-10) vs 6.5(6-7.75)] and computed tomography (CTP) brain blood volume (CBV)[9(8-10) vs 4(2-7.75)], cerebral blood flow (CBF)[7(4.5-9) vs 2(1-4)], time to peak (Tmax) [1(0.5-4) vs 0(0-1.75)] imaging of the posterior circulation Alberta stroke project early CT score (pc-ASPECTS score), Different locations of vascular occlusion, time from femoral artery puncture to vascular recanalization(64.96 ± 33.47 vs 92.68 ± 53.17). The differences in the conversion rate of postoperative intracranial hemorrhage(0 vs 16.1%) and the incidence of sICH(0 vs 12.9%) were statistically significant (P < 0.05). The subgroup analysis showed that vascular occlusion site, preoperative CBV pc-ASPECTS scores, and postoperative sICH occurrence were related to the 90-day prognosis of patients, and the differences were statistically significant (P < 0.05). CONCLUSIONS: Some factors that can affect the prognosis of mechanical thrombectomy in patients with acute posterior circulation large vessel occlusion cerebral infarction. Preoperative clinical symptoms and imaging evaluation have certain evaluation values for prognosis.