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BACKGROUND: Historically, tobacco companies have used flavored tobacco products to enhance the appeal of tobacco consumption, encourage initiation and experimentation of tobacco use, and contribute to sustained tobacco use. While flavored tobacco products are regulated in several countries, there is no existing regulation on flavored tobacco products in the Philippines, specifically for cigarettes and cigars. This study aims to update evidence on the flavored tobacco product landscape in the Philippines by assessing both the flavor descriptors and flavor imagery featured on cigarette and cigar packaging. RESULTS: We collected 106 cigarette and cigar products from four major cities in the National Capital Region, Balanced Luzon, Visayas, and Mindanao. Of these 106 cigarette and cigar products, 62 (58.49%) had flavor descriptors. Three crushable capsule products did not feature any flavor descriptor but were included for flavor imagery examination. We identified five categories of flavor descriptors: menthol, concept descriptors, tobacco, beverages, and other flavors. Out of 62 packs, ten featured more than one flavor descriptor on the packaging. Menthol flavor descriptors comprised the majority of flavor descriptors. Imagery and other graphic elements closely resonate with and enhance the flavor descriptors found on these packs. CONCLUSIONS: This study aimed to update the evidence on the flavored tobacco product landscape in the Philippines and address their absence of regulation. Regulating flavored tobacco products requires a comprehensive policy approach complemented by complete enforcement. Flavor substances, flavor descriptors, and flavor imagery must be regulated altogether; however, it is ideal to enforce a ban on flavored tobacco products in compliance with the WHO FCTC, to which the Philippines is a signatory. Policymakers should consider plain packaging as an intervention to eliminate the appeals associated with flavored tobacco products.

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The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.

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BACKGROUND: Colorectal cancer is the third most common tumour entity in the world and up to 50% of the patients develop liver metastases (CRLM) within five years. To improve and personalize therapeutic strategies, new diagnostic tools are urgently needed. For instance, biomechanical tumour properties measured by magnetic resonance elastography (MRE) could be implemented as such a diagnostic tool. We postulate that ex vivo MRE combined with histological and radiological evaluation of CRLM could provide biomechanics-based diagnostic markers for cell viability in tumours. METHODS: 34 CRLM specimens from patients who had undergone hepatic resection were studied using ex vivo MRE in a frequency range from 500 Hz to 5300 Hz with increments of 400 Hz. Single frequency evaluation of shear wave speed and wave penetration rate as proxies for stiffness and viscosity was performed, along with rheological model fitting based on the spring-pot model and powerlaw exponent α, ranging between 0 (complete solid behaviour) and 1 (complete fluid behaviour). For histological analysis, samples were stained with H&E and categorized according to the degree of regression. Quantitative histologic analysis was performed to analyse nucleus size, aspect ratio, and density. Radiological response was assessed according to RECIST-criteria. RESULTS: Five samples showed major response to chemotherapy, six samples partial response and 23 samples no response. For higher frequencies (> 2100 Hz), shear wave speed correlated significantly with the degree of regression (p ≤ 0.05) indicating stiffer properties with less viable tumour cells. Correspondingly, rheological analysis of α revealed more elastic-solid tissue properties at low cell viability and major response (α = 0.43 IQR 0.36, 0.47) than at higher cell viability and no response (α = 0.51 IQR 0.48, 0.55; p = 0.03). Quantitative histological analysis showed a decreased nuclear area and density as well as a higher nuclear aspect ratio in patients with major response to treatment compared to patients with no response (all p < 0.05). DISCUSSION: Our results suggest that MRE could be useful in the characterization of biomechanical property changes associated with cell viability in CRLM. In the future, MRE could be applied in clinical diagnosis to support individually tailored therapy plans for patients with CRLM.

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Soft bioelectronic neural interfaces have great potential as mechanically favourable alternatives to implantable metal electrodes. In this pursuit, conductive hydrogels (CHs) are particularly viable, combining tissue compliance with the required electrochemical characteristics. Physically-aggregated CHs offer an additional advantage by their facile synthesis into injectable systems, enabling minimally invasive implantation, though they can be impeded by a lack of control over their particle size and packing. Guided by these principles, an injectable PEDOT:PSS/acetic acid-based hydrogel is presented herein whose mechanical and electrochemical properties are independently tuneable by modifying the relative acetic acid composition. The fabrication process further benefits from employing batch emulsion to decrease particle sizes and facilitate tighter packing. The resulting material is stable and anatomically compact upon injection both in tissue phantom and ex vivo, while retaining favourable electrochemical properties in both contexts. Biphasic current stimulation yielding voltage transients well below the charge injection limit as well as the gel's non-cytotoxicity further underscore its potential for safe and effective neural interfacing applications.

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The extracellular environment plays a crucial role in many physiological and pathological processes involving cell motility, such as metastatic invasion in cancer development, by heavily impacting the migration strategies adopted by the cells. The study of how mechanical constraints affect the dynamics of cell migration may be relevant to gain more insight into such processes, and it may prove to be a powerful tool in the hands of biologists. In this chapter, we describe the methods used to investigate the ability of neoplastic cells to migrate through narrowing, rigid microstructures upon chemoattractant stimulation.

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Complementary approaches (stomach contents, DNA barcoding, and stable isotopes) were used to examine seasonal shifts in the feeding ecology of an oceanic predator, yellowfin tuna (Thunnus albacares, n = 577), in the northern Gulf of Mexico. DNA barcoding greatly enhanced dietary resolution and seasonally distinct prey assemblages were observed for both sub-adults and adults. In general, diet was characterized by ommastrephid squids and exocoetids in spring, juvenile fishes (i.e., carangids and scombrids) in summer, migratory coastal fishes during fall, and an increased consumption of planktonic prey (e.g., amphipods) in winter. Seasonal variability in bulk stable isotope values (δ13C, δ15N, and δ34S) was also observed, with low δ15N values and high δ34S values during late summer/early fall and high δ15N values (low δ34S) during late winter/early spring. Bayesian stable isotope mixing models corroborated seasonal diet shifts, highlighting the importance of oceanic nekton in spring/summer, coastal nekton during fall, and oceanic plankton during winter. Seasonal shifts in diet appeared to be influenced by prey reproductive cycles, habitat associations, and environmental conditions. Findings highlight the complex food web dynamics supporting an opportunistic oceanic predator and the importance of seasonal cycles in prey availability to predator resource utilization in open-ocean ecosystems.

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Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges-an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

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BACKGROUND: Insomnia is a highly prevalent disorder associated with numerous adverse health outcomes. Cognitive behavioural therapy for insomnia (CBT-I) is recommended as first-line treatment by clinical guidelines but is accessible to only a minority of patients suffering from insomnia. Internet-delivered CBT-I (iCBT-I) could contribute to the widespread dissemination of this first-line treatment. As there is insufficient evidence regarding non-inferiority, this study directly aims to compare therapist-guided internet-delivered versus face-to-face CBT-I in terms of insomnia severity post-treatment. Furthermore, a health-economic evaluation will be conducted, and potential benefits and disadvantages of therapist-guided iCBT-I will be examined. METHODS: This study protocol describes a randomised controlled two-arm parallel-group non-inferiority trial comparing therapist-guided iCBT-I with face-to-face CBT-I in routine clinical care. A total of 422 patients with insomnia disorder will be randomised and treated at 16 study centres throughout Germany. Outcomes will be assessed at baseline, 10 weeks after randomisation (post), and 6 months after randomisation (follow-up). The primary outcome is insomnia severity measured using the Insomnia Severity Index. Secondary outcomes include depression-related symptoms, quality of life, fatigue, physical activity, daylight exposure, adverse events related to treatment, and a health-economic evaluation. Finally, potential moderator variables and several descriptive and exploratory outcomes will be assessed (e.g. benefits and disadvantages of internet-delivered treatment). DISCUSSION: The widespread implementation of CBT-I is a significant healthcare challenge. The non-inferiority of therapist-guided iCBT-I versus face-to-face CBT-I will be investigated in an adequately powered sample in routine clinical care, with the same therapeutic content and same level of therapist qualifications provided with both interventions. If this trial demonstrates the non-inferiority of therapist-guided iCBT-I, healthcare providers may be more confident recommending this treatment to their patients, contributing to the wider dissemination of CBT-I. TRIAL REGISTRATION: Trial registration number in the German Clinical Trials Register: DRKS00028153 ( https://drks.de/search/de/trial/DRKS00028153 ). Registered on 16th May 2023.

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The advent of closed-loop bionics has created a demand for electrode materials that are ideal for both stimulating and recording applications. The growing complexity and diminishing size of implantable devices for neural interfaces have moved beyond what can be achieved with conventional metallic electrode materials. Polymeric electrode materials are a recent development based on polymer composites of organic conductors such as conductive polymers. These materials present exciting new opportunities in the design and fabrication of next-generation electrode arrays which can overcome the electrochemical and mechanical limitations of conventional electrode materials. This review will examine the recent developments in polymeric electrode materials, their application as stimulating and recording electrodes in bionic devices, and their impact on the development of soft, conformal, and high-density neural interfaces.

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Cells can adapt their active contractile properties to switch between dynamical migratory states and static homeostasis. Collective tissue surface tension, generated among others by the cortical contractility of single cells, can keep cell clusters compact, while a more bipolar, anisotropic contractility is predominantly used by mesenchymal cells to pull themselves into the extracellular matrix (ECM). Here, we investigate how these two contractility modes relate to cancer cell escape into the ECM. We compare multicellular spheroids from a panel of breast cancer cell lines with primary tumor explants from breast and cervical cancer patients by measuring matrix contraction and cellular spreading into ECM mimicking collagen matrices. Our results in spheroids suggest that tumor aggressiveness is associated with elevated contractile traction and reduced active tissue surface tension, allowing cancer cell escape. We show that it is not a binary switch but rather the interplay between these two contractility modes that is essential during this process. We provide further evidence in patient-derived tumor explants that these two contractility modes impact cancer cells' ability to leave cell clusters within a primary tumor. Our results indicate that cellular contractility is an essential factor during the formation of metastases and thus may be suitable as a prognostic criterion for the assessment of tumor aggressiveness.

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BACKGROUND: Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk. METHODS: Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of BMs (AURORA) retrospective study (training cohort: 253 patients from 2 centers; external test cohort: 99 patients from 5 centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (T2-FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set. RESULTS: The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (P < .001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively. CONCLUSIONS: A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy.

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Background: The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. Methods: One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans. Results: The parameter ratio Dw/ρ (P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans. Conclusions: Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.

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Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

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In cell clusters, the prominent factors at play encompass contractility-based enhanced tissue surface tension and cell unjamming transition. The former effect pertains to the boundary effect, while the latter constitutes a bulk effect. Both effects share outcomes of inducing significant elongation in cells. This elongation is so substantial that it surpasses the limits of linear elasticity, thereby giving rise to additional effects. To investigate these effects, we employ atomic force microscopy (AFM) to analyze how the mechanical properties of individual cells change under such considerable elongation. Our selection of cell lines includes MCF-10A, chosen for its pronounced demonstration of the extended differential adhesion hypothesis (eDAH), and MDA-MB-436, selected due to its manifestation of cell unjamming behavior. In the AFM analyses, we observe a common trend in both cases: as elongation increases, both cell lines exhibit strain stiffening. Notably, this effect is more prominent in MCF-10A compared to MDA-MB-436. Subsequently, we employ AFM on a dynamic range of 1-200 Hz to probe the mechanical characteristics of cell spheroids, focusing on both surface and bulk mechanics. Our findings align with the results from single cell investigations. Specifically, MCF-10A cells, characterized by strong contractile tissue tension, exhibit the greatest stiffness on their surface. Conversely, MDA-MB-436 cells, which experience significant elongation, showcase their highest stiffness within the bulk region. Consequently, the concept of single cell strain stiffening emerges as a crucial element in understanding the mechanics of multicellular spheroids (MCSs), even in the case of MDA-MB-436 cells, which are comparatively softer in nature.

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Recent trends in the field of bioelectronics have been focused on the development of electrodes that facilitate safe and efficient stimulation of nervous tissues. Novel conducting polymer (CP) based materials, such as flexible and fully polymeric conductive elastomers (CEs), constitute a promising alternative to improve on the limitations of current metallic devices. This pilot study demonstrates the performance of tripolar CE-based peripheral nerve cuffs compared to current commercial tripolar platinum-iridium (PtIr) nerve cuffs in vivo. CE and metallic cuff devices were implanted onto rodent sciatic nerves for a period of 8 weeks. Throughout the entire study, the CE device demonstrated improved charge transfer and electrochemical safety compared to the PtIr cuff, able to safely inject 2 to 3 times more charge. In comparison to the commercial control, the CE cuff was able to record in the in vivo setting with reduced noise and produced smaller voltages at all simulation levels. CE technologies provide a promising alternative to metallic devices for the development of bioelectronics with enhanced chronic device functionality.

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This work highlights the development of a conductive elastomer (CE) based electrophoretic platform that enables the transfer of charged molecules from a solid-state CE electrode directly to targeted tissues. Using an elastomer-based electrode containing poly (3,4-ethylenedioxythiophene) nanowires, controlled electrophoretic delivery of methylene blue (MB) and fluorescein (FLSC) was achieved with applied voltage. Electroactive release of positively charged MB and negatively charged FLSC achieved 33.19 ± 6.47 µg release of MB and 22.36 ± 3.05 µg release of FLSC, a 24 and 20-fold increase in comparison to inhibitory voltages over 1 h. Additionally, selective, and sequential release of the two oppositely charged molecules from a single CE device was demonstrated, showing the potential of this device to be used in multi-drug treatments.

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In line with Article 11 of the WHO Framework Convention on Tobacco Control, the Philippines has implemented graphic health warnings on cigarette packs. To date, there has been no published literature evaluating the perceived effectiveness of GHWs in the Philippines. This study aims to contribute to the evidence on the perceived effects of graphic health warnings (GHWs) in cigarette packaging and the potential impact of plain packaging in the Philippines. The study involved an online convenience survey and a nationwide household survey. Mock-up sets of cigarette packs based on the Philippines' law on GHWs, and Thailand's and Singapore's plain packaging were shown to respondents to rate their attractiveness, quality, taste, cost, social appeal, appeal to youth, noticeability, appeal to non-smokers, attempt to quit, ease of quitting, discouraging smoking, and perceived harm to health on a five-point Lickert scale. The online and household surveys recruited 2,701 respondents in total. Online and household survey respondents considered plain packaging with larger graphic health warnings and visible quitlines from Thailand and Singapore to be more effective in discouraging them from smoking. Both sets of survey respondents also found mock-ups from Thailand and Singapore more motivating for them to attempt quitting than cigarette pack mock-ups from the Philippines. The study concludes that current graphic health warnings in the Philippines are ineffective in instilling health consciousness among Filipinos. Policymakers should consider larger graphic health warnings and plain packaging of cigarettes to motivate smokers to quit and discourage Filipinos from smoking.

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Gene expression signatures refer to patterns of gene activities and are used to classify different types of cancer, determine prognosis, and guide treatment decisions. Advancements in high-throughput technology and machine learning have led to improvements to predict a patient's prognosis for different cancer phenotypes. However, computational methods for analyzing signatures have not been used to evaluate their prognostic power. Contention remains on the utility of gene expression signatures for prognosis. The prevalent approaches include random signatures, expert knowledge, and machine learning to construct an improved signature. We unify these approaches to evaluate their prognostic power. Re-evaluation of publicly available gene-expression data from 8 databases with 9 machine-learning models revealed previously unreported results. Gene-expression signatures are confirmed to be useful in predicting a patient's prognosis. Convergent evidence from [Formula: see text] 10,000 signatures implicates a maximum prognostic power. By calculating the concordance index, which measures how well patients with different prognoses can be discriminated, we show that a signature can correctly discriminate patients' prognoses no more than 80% of the time. Additionally, we show that more than 50% of the potentially available information is still missing at this value. We surmise that an accurate prognosis must incorporate molecular, clinical, histological, and other complementary factors.

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BACKGROUND: Many automatic approaches to brain tumor segmentation employ multiple magnetic resonance imaging (MRI) sequences. The goal of this project was to compare different combinations of input sequences to determine which MRI sequences are needed for effective automated brain metastasis (BM) segmentation. METHODS: We analyzed preoperative imaging (T1-weighted sequence ± contrast-enhancement (T1/T1-CE), T2-weighted sequence (T2), and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence) from 339 patients with BMs from seven centers. A baseline 3D U-Net with all four sequences and six U-Nets with plausible sequence combinations (T1-CE, T1, T2-FLAIR, T1-CE + T2-FLAIR, T1-CE + T1 + T2-FLAIR, T1-CE + T1) were trained on 239 patients from two centers and subsequently tested on an external cohort of 100 patients from five centers. RESULTS: The model based on T1-CE alone achieved the best segmentation performance for BM segmentation with a median Dice similarity coefficient (DSC) of 0.96. Models trained without T1-CE performed worse (T1-only: DSC = 0.70 and T2-FLAIR-only: DSC = 0.73). For edema segmentation, models that included both T1-CE and T2-FLAIR performed best (DSC = 0.93), while the remaining four models without simultaneous inclusion of these both sequences reached a median DSC of 0.81-0.89. CONCLUSIONS: A T1-CE-only protocol suffices for the segmentation of BMs. The combination of T1-CE and T2-FLAIR is important for edema segmentation. Missing either T1-CE or T2-FLAIR decreases performance. These findings may improve imaging routines by omitting unnecessary sequences, thus allowing for faster procedures in daily clinical practice while enabling optimal neural network-based target definitions.