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Detecting and screening clouds is the first step in most optical remote sensing analyses. Cloud formation is diverse, presenting many shapes, thicknesses, and altitudes. This variety poses a significant challenge to the development of effective cloud detection algorithms, as most datasets lack an unbiased representation. To address this issue, we have built CloudSEN12+, a significant expansion of the CloudSEN12 dataset. This new dataset doubles the expert-labeled annotations, making it the largest cloud and cloud shadow detection dataset for Sentinel-2 imagery up to date. We have carefully reviewed and refined our previous annotations to ensure maximum trustworthiness. We expect CloudSEN12+ will be a valuable resource for the cloud detection research community.

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This Proposed work explores how machine learning can be used to diagnose conjunctivitis, a common eye ailment. The main goal of the study is to capture eye images using camera-based systems, perform image pre-processing, and employ image segmentation techniques, particularly the UNet++ and U-net models. Additionally, the study involves extracting features from the relevant areas within the segmented images and using Convolutional Neural Networks for classification. All this is carried out using TensorFlow, a well-known machine-learning platform. The research involves thorough training and assessment of both the UNet and U-net++ segmentation models. A comprehensive analysis is conducted, focusing on their accuracy and performance. The study goes further to evaluate these models using both the UBIRIS dataset and a custom dataset created for this specific research. The experimental results emphasize a substantial improvement in the quality of segmentation achieved by the U-net++ model, the model achieved an overall accuracy of 97.07. Furthermore, the UNet++ architecture displays better accuracy in comparison to the traditional U-net model. These outcomes highlight the potential of U-net++ as a valuable advancement in the field of machine learning-based conjunctivitis diagnosis.

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The development of adsorbents for efficient and highly selective seawater extraction of uranium was instrumental in fostering sustainable progress in energy and addressing the prevailing energy crisis. However, the complex background composition of the marine environment, including radionuclides, organic pollutants, and a large number of co-existing heavy metal ions, were non-negligible obstacles to the extraction of uranium from seawater. The present investigation successfully employed a self-templated approach to synthesize porous nitrogen-doped carbon (PNC) derived from COF, which exhibited tremendous potential as an adsorbent for pollutant removal in environmental treatment. LZU1@PNC not only retained the structural features of the original COF-LZU1, but also overcame the acid-base instability problem commonly found in COFs. Subsequently, the removal process of two typical water pollutants on the material was investigated using 2,4-DCP and [UO2(CO3)3]4-. The results demonstrated that LZU1@PNC exhibited superior removal performance for the target pollutants compared to COF-LZU1, owing to its larger specific surface area and abundant defect structure. After six desorption-regeneration cycles, LZU1@PNC still maintained a high removal rate of the target contaminants, demonstrating the stability of this material and its excellent recyclability. In addition, based on various characterization techniques, the removal mechanism of 2,4-DCP was presumed to be mainly electrostatic attraction, hydrogen bonding, and π-π stacking interactions. Conversely, the elimination process of [UO2(CO3)3]4- predominantly relied on surface complexation phenomena. The present investigation provided new perspectives and stimulated a broader study of other COF-derived carbon materials and their modifications as adsorbents for uranium extraction from seawater and other applications.

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OBJECTIVE: As metabolic dysfunction-associated steatotic liver disease (MASLD) becomes more prevalent worldwide, it is imperative to create more accurate technologies that make it easy to assess the liver in a point-of-care setting. The aim of this study is to test the performance of a new software tool implemented in Velacur (Sonic Incytes), a liver stiffness and ultrasound attenuation measurement device, on patients with MASLD. This tool employs a deep learning-based method to detect and segment shear waves in the liver tissue for subsequent analysis to improve tissue characterization for patient diagnosis. METHODS: This new tool consists of a deep learning based algorithm, which was trained on 15,045 expert-segmented images from 103 patients, using a U-Net architecture. The algorithm was then tested on 4429 images from 36 volunteers and patients with MASLD. Test subjects were scanned at different clinics with different Velacur operators. Evaluation was performed on both individual images (image based) and averaged across all images collected from a patient (patient based). Ground truth was defined by expert segmentation of the shear waves within each image. For evaluation, sensitivity and specificity for correct wave detection in the image were calculated. For those images containing waves, the Dice coefficient was calculated. A prototype of the software tool was also implemented on Velacur and assessed by operators in real world settings. RESULTS: The wave detection algorithm had a sensitivity of 81% and a specificity of 84%, with a Dice coefficient of 0.74 and 0.75 for image based and patient-based averages respectively. The implementation of this software tool as an overlay on the B-Mode ultrasound resulted in improved exam quality collected by operators. CONCLUSION: The shear wave algorithm performed well on a test set of volunteers and patients with metabolic dysfunction-associated steatotic liver disease. The addition of this software tool, implemented on the Velacur system, improved the quality of the liver assessments performed in a real world, point of care setting.

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Thermography is a non-invasive and non-contact method for detecting cancer in its initial stages by examining the temperature variation between both breasts. Preprocessing methods such as resizing, ROI (region of interest) segmentation, and augmentation are frequently used to enhance the accuracy of breast thermogram analysis. In this study, a modified U-Net architecture (DTCWAU-Net) that uses dual-tree complex wavelet transform (DTCWT) and attention gate for breast thermal image segmentation for frontal and lateral view thermograms, aiming to outline ROI for potential tumor detection, was proposed. The proposed approach achieved an average Dice coefficient of 93.03% and a sensitivity of 94.82%, showcasing its potential for accurate breast thermogram segmentation. Classification of breast thermograms into healthy or cancerous categories was carried out by extracting texture- and histogram-based features and deep features from segmented thermograms. Feature selection was performed using Neighborhood Component Analysis (NCA), followed by the application of machine learning classifiers. When compared to other state-of-the-art approaches for detecting breast cancer using a thermogram, the proposed methodology showed a higher accuracy of 99.90% for VGG16 deep features with NCA and Random Forest classifier. Simulation results expound that the proposed method can be used in breast cancer screening, facilitating early detection, and enhancing treatment outcomes.

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This data paper presents a reconstruction of American women who have served in state and territorial legislatures (female legislators). The dataset spans from 1895 to 1995 and is reported at an annual basis. For all 6,466 women, individual information on each female legislator is provided, including their name, surname, party affiliation, city and county of residence, and the state they represented. Data for the Senate and House are reported separately. The data was extracted from the encyclopedia titled ``Women State and Territorial Legislators, 1895-1995. A State-by-State Analysis, with Rosters of 6,000 Women'' [1]. The dataset can be used to study patterns in political representation, assessing the involvement of women in government, and delving into significant themes such as the intersection between women legislators and the historical, cultural, and political dynamics of their era. The categorization of women according to their city/county of residence enables researchers to seamlessly integrate this data with other spatio-temporal databases. Additionally, the dataset includes the FIPS county codes corresponding to each woman's residence, facilitating convenient linkage with other datasets, such as census data, using the FIPS code.

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Emotions play an important role in fostering positive parenting and healthy child development. This qualitative study explored the affective experiences of racially diverse US fathers with low income across the prenatal, postnatal, and early childhood periods. Semi-structured interviews were conducted with 24 fathers. Interview questions asked about fathers' early parenting experiences that elicit parenting emotions of different valence. Results from thematic analysis demonstrated activation of multiple emotions depending on different proximal and distal experiences. Specific to proximal experiences, fathers reported feeling both excited and anxious about pregnancy and joyful and disappointed at childbirth. Related to distal experiences, fathers reported feeling encouraged by their social support networks that further aid their parenting, but feeling marginalized given systematic barriers (e.g., societal bias, high incarceration rates of Black fathers). Most importantly, fathers' parenting emotions, especially negative ones, led to them resolving to stay involved in their children's lives, gaining a sense of responsibility, and changing behaviors to do right by their children. Fathers resorted to various coping strategies to regulate their negative emotions. Overall, fathers with low income are emotionally resilient. Infant and early childhood health professionals should support fathers' mental health to promote father-child engagement and thus, ultimately, young children's mental health and wellbeing.

Las emociones juegan un papel importante en fomentar una crianza positiva y un saludable desarrollo del niño. Este estudio cualitativo exploró las experiencias afectivas de papás de Estados Unidos de bajos recursos económicos que son racialmente diversos a lo largo de los períodos prenatal, postnatal y la temprana niñez. Se llevaron a cabo entrevistas semiestructuradas con 24 papás. Las preguntas de la entrevista trataban acerca de las tempranas experiencias de crianza de los papás que provocaban emociones de crianza de valencia diferente. Los resultados de análisis temáticos demostraron la activación de múltiples emociones dependiendo de diferentes emociones proximales y distales. Específico a las experiencias proximales, los papás reportaron sentirse tanto emocionados como ansiosos acerca del embarazo y alegres y decepcionados al momento del nacimiento. Con relación a las experiencias distales, los papás reportaron sentirse animados por parte de sus redes de apoyo social que ayudaron en su acercamiento a la crianza y sentirse marginalizados dadas las barreras sistemáticas. De manera más importante, las emociones de crianza de los papás especialmente las negativas, les llevaron a decidir mantenerse involucrados en las vidas de sus niños, adquiriendo un sentido de responsabilidad y cambiando conductas para hacer lo correcto con sus niños. Los papás recurrieron a varias estrategias para regular sus emociones negativas. En general, los papás de bajas entradas económicas son emocionalmente fuertes. Los profesionales de la salud infantil y en la temprana niñez deben apoyar la salud mental de los papás para promover la compenetración papá­niño y a la larga, la salud mental y el bienestar de los niños pequeños.

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OBJECTIVE: A fully automated laminar cartilage composition (MRI-based T2) analysis method was technically and clinically validated by comparing radiographically normal knees with (CL-JSN) and without contra-lateral joint space narrowing or other signs of radiographic osteoarthritis (OA, CL-noROA). MATERIALS AND METHODS: 2D U-Nets were trained from manually segmented femorotibial cartilages (n = 72) from all 7 echoes (AllE), or from the 1st echo only (1stE) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the AllE U-Net was then applied to knees from the OAI healthy reference cohort (n = 10), CL-JSN (n = 39), and (1:1) matched CL-noROA knees (n = 39) that all had manual expert segmentation, and to 982 non-matched CL-noROA knees without expert segmentation. RESULTS: The agreement (Dice similarity coefficient) between automated vs. manual expert cartilage segmentation was between 0.82 ± 0.05/0.79 ± 0.06 (AllE/1stE) and 0.88 ± 0.03/0.88 ± 0.03 (AllE/1stE) across femorotibial cartilage plates. The deviation between automated vs. manually derived laminar T2 reached up to - 2.2 ± 2.6 ms/ + 4.1 ± 10.2 ms (AllE/1stE). The AllE U-Net showed a similar sensitivity to cross-sectional laminar T2 differences between CL-JSN and CL-noROA knees in the matched (Cohen's D ≤ 0.54) and the non-matched (D ≤ 0.54) comparison as the matched manual analyses (D ≤ 0.48). Longitudinally, the AllE U-Net also showed a similar sensitivity to CL-JSN vs. CS-noROA differences in the matched (D ≤ 0.51) and the non-matched (D ≤ 0.43) comparison as matched manual analyses (D ≤ 0.41). CONCLUSION: The fully automated T2 analysis showed a high agreement, acceptable accuracy, and similar sensitivity to cross-sectional and longitudinal laminar T2 differences in an early OA model, compared with manual expert analysis. TRIAL REGISTRATION: Clinicaltrials.gov identification: NCT00080171.

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BACKGROUND: Liver cancer poses a significant health challenge due to its high incidence rates and complexities in detection and treatment. Accurate segmentation of liver tumors using medical imaging plays a crucial role in early diagnosis and treatment planning. OBJECTIVE: This study proposes a novel approach combining U-Net and ResNet architectures with the Adam optimizer and sigmoid activation function. The method leverages ResNet's deep residual learning to address training issues in deep neural networks. At the same time, U-Net's structure facilitates capturing local and global contextual information essential for precise tumor characterization. The model aims to enhance segmentation accuracy by effectively capturing intricate tumor features and contextual details by integrating these architectures. The Adam optimizer expedites model convergence by dynamically adjusting the learning rate based on gradient statistics during training. METHODS: To validate the effectiveness of the proposed approach, segmentation experiments are conducted on a diverse dataset comprising 130 CT scans of liver cancers. Furthermore, a state-of-the-art fusion strategy is introduced, combining the robust feature learning capabilities of the UNet-ResNet classifier with Snake-based Level Set Segmentation. RESULTS: Experimental results demonstrate impressive performance metrics, including an accuracy of 0.98 and a minimal loss of 0.10, underscoring the efficacy of the proposed methodology in liver cancer segmentation. CONCLUSION: This fusion approach effectively delineates complex and diffuse tumor shapes, significantly reducing errors.

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Liver cancer is one of the malignancies with high mortality rates worldwide, and its timely detection and accurate diagnosis are crucial for improving patient prognosis. To address the limitations of traditional image segmentation techniques and the U-Net network in capturing fine image features, this study proposes an improved model based on the U-Net architecture, named RHEU-Net. By replacing traditional convolution modules in the encoder and decoder with improved residual modules, the network's feature extraction capabilities and gradient stability are enhanced. A Hybrid Gated Attention (HGA) module is integrated before the skip connections, enabling the parallel processing of channel and spatial attentions, optimizing the feature fusion strategy, and effectively replenishing image details. A Multi-Scale Feature Enhancement (MSFE) layer is introduced at the bottleneck, utilizing multi-scale feature extraction technology to further enhance the expression of receptive fields and contextual information, improving the overall feature representation effect. Testing on the LiTS2017 dataset demonstrated that RHEU-Net achieved Dice scores of 95.72% for liver segmentation and 70.19% for tumor segmentation. These results validate the effectiveness of RHEU-Net and underscore its potential for clinical application.

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Metal additive manufacturing (AM) is a disruptive technology that provides unprecedented design freedom and manufacturing flexibility for the forming of complex components. Despite its unparalleled advantages over traditional manufacturing methods, the existence of fatal issues still seriously hinders its large-scale industrial application. Against this backdrop, U-FAAM is emerging as a focus, integrating ultrasonic energy into conventional metal AM processes to harness distinctive advantages. This work offers an up-to-date, specialized review of U-FAAM, articulating the integrated modes, mechanisms, pivotal research achievements, and future development trends in a systematic manner. By synthesizing existing research, it highlights future directions in further optimizing process parameters, expanding material applicability, etc., to advance the industrial application and development of U-FAAM technology.

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BACKGROUND: Limited research has investigated the association between training load and performance of basketball players during games. Little is known about how different indicators of player performance are affected by internal and external loads. The purpose of this study was to determine whether external and internal loads influence basketball players' performance during games. METHOD: This longitudinal study involved 20 professional male basketball players from a single team, classified as first-level athletes by the Chinese Basketball Association. During 34 games, external load was measured as PlayerLoad using micro-sensors, while internal load was assessed using session rating of perceived exertion (sRPE). Player performance was quantified using three metrics: Efficiency, Player Index Rating (PIR), and Plus-Minus (PM). Pearson correlation coefficients were calculated to assess the strength of the relationships between training loads and performance metrics. Linear mixed-effects models were applied to further analyze the influence of internal and external loads on basketball performance. RESULTS: Pearson correlation analysis revealed moderate positive correlations between both sRPE and PlayerLoad with Efficiency and PIR. Specifically, sRPE (r = 0.52) and PlayerLoad (r = 0.54) were both significantly correlated with Efficiency. For PIR, sRPE (r = 0.50) and PlayerLoad (r = 0.56) also demonstrated moderate correlations. These correlations were further substantiated by linear mixed-effects models, which showed that sRPE (ß = 2.21, p < 0.001) and PlayerLoad (ß = 1.87, p = 0.004) had significant independent effects on Efficiency. Similarly, sRPE (ß = 2.15, p < 0.001) and PlayerLoad (ß = 2.36, p < 0.001) significantly predicted PIR. Additionally, a significant interaction effect between PlayerLoad and sRPE was found on Plus-Minus (ß = -2.49, p < 0.001), indicating that the combination of high physical and psychological loads negatively impacted overall team performance. However, the correlation strengths for Plus-Minus were relatively low (sRPE: r = 0.16; PlayerLoad: r = 0.10). CONCLUSION: Both external and internal loads positively contribute to performance, the integration of objective (accelerometry) and subjective (sRPE) measures of load provides a comprehensive understanding of the physiological and psychological demands on athletes, contributing to more effective training regimens and performance optimization.

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BACKGROUND: The hypothesis of changing network layers to increase the accuracy of dose distribution prediction, instead of expanding their dimensions, which requires complex calculations, has been considered in our study. MATERIALS AND METHODS: A total of 137 prostate cancer patients treated with the tomotherapy technique were categorized as 80% training and validating as well as 20% testing for the nested UNet and UNet architectures. Mean absolute error (MAE) was used to measure the dosimetry indices of dose-volume histograms (DVHs), and geometry indices, including the structural similarity index measure (SSIM), dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC), were used to evaluate the isodose volume (IV) similarity prediction. To verify a statistically significant difference, the two-way statistical Wilcoxon test was used at a level of 0.05 (p < 0.05). RESULTS: Use of a nested UNet architecture reduced the predicted dose MAE in DVH indices. The MAE for planning target volume (PTV), bladder, rectum, and right and left femur were D98% = 1.11 ± 0.90; D98% = 2.27 ± 2.85, Dmean = 0.84 ± 0.62; D98% = 1.47 ± 12.02, Dmean = 0.77 ± 1.59; D2% = 0.65 ± 0.70, Dmean = 0.96 ± 2.82; and D2% = 1.18 ± 6.65, Dmean = 0.44 ± 1.13, respectively. Additionally, the greatest geometric similarity was observed in the mean SSIM for UNet and nested UNet (0.91 vs. 0.94, respectively). CONCLUSION: The nested UNet network can be considered a suitable network due to its ability to improve the accuracy of dose distribution prediction compared to the UNet network in an acceptable time.

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Titanite-bearing calc-silicates and mafic gneisses, metamorphosed under amphibolite- to granulite-facies conditions, crop out in Val d'Ossola area (Ivrea-Verbano Zone, Italy). The Ivrea-Verbano Zone represents an exhumed section of the pre-Alpine middle to lower continental crust which escaped the Alpine subduction, thus provides a unique opportunity to study continental crustal processes and evolution. Among several samples, three, collected from different locations, were chosen for detailed analyses of titanite. Petrochronology of titanite was performed with Laser ablation split-stream (LASS) technique on petrographic thin sections. Petrochronological results on titanite do not define clear correlations with chemistry except for one sample. Rare earth elements (REE) patterns of titanite from the three samples are apparently different in terms of average concentration (i.e., lower or upper 1000 times CI), shapes and occurrence or absence of Eu negative anomaly. Al/Fe vs ΣLREE and Fe content vs Zr/Y plots show that the studied samples coincide with metamorphic rock field deriving from calc-silicates and mafic protoliths, as previously demonstrated in literature. Any compilations of petrochronological data on titanite from the metamorphic volcano-sedimentary sequence of Val d'Ossola can be found in literature. Therefore, these data represent a new insight on an accessory mineral phase whose significance and scientific interest are rising in the last years. Future studies of the evolution of these kinds of rock, widespread in the high-grade metamorphic basements, will benefit from these data as a term of comparison.

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Mutations in SF3B1 are common in many types of cancer, which promotes cancer progression through aberrant RNA splicing. Recently, mRNA nuclear export has been reported to be defective in cells with SF3B1 K700E mutation. However, the mechanism remains unclear. Our study reveals that the K700E mutation in SF3B1 attenuates its interaction with THOC5, an essential component of mRNA nuclear export complex THO. Furthermore, SF3B1 mutation caused reduced binding of THOC5 with some mRNA and inhibited the nuclear export of these mRNA. Interestingly, THOC5 overexpression restores the nuclear export of these mRNA in cells with SF3B1 K700E mutation. Importantly, other types of cancer-associated SF3B1 mutations also inhibited mRNA nuclear export similarly, suggesting that it is common for cancer-associated SF3B1 mutation to inhibit mRNA nuclear export. Our research highlights the critical role of the THOC5-SF3B1 interaction in the regulation of mRNA nuclear export and provides valuable insights into the impact of SF3B1 mutations on mRNA nuclear export.

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Parents' language use is an important context for early socialization. We examined the relationship between parents' self-reported mindfulness and observed language use in two forms of attachment-relevant communication. Sixty-three parents of 6-18-month-old infants from Australia (n = 32) and New Zealand (n = 31) completed the five facets of mindfulness-short form (FFMQ-SF) questionnaire, the adult attachment interview (AAI), and a 10-min play session with their infant. We examined parents' frequency of word usage within the categories of the linguistic inquiry word count (LIWC) text analysis program to explore the relationship between mindfulness and language use. Mindfulness was associated with cognitive, affective, perceptual, and time orientation language use in the AAI. However, fewer associations were identified between mindfulness and language use in the parent-infant play session. Results are discussed in terms of their relevance to mindfulness and attachment.

El uso del lenguaje por parte de los padres es un contexto importante para la temprana socialización. Examinamos la relación entre la auto reportada atención consciente de los padres y el observado uso del lenguaje en dos formas de comunicación relevantes a la unión afectiva. Sesenta y tres progenitores de infantes entre 6 y 18 meses de edad de Australia (n = 32) y Nueva Zelanda (n = 31) completaron el cuestionario de Cinco Facetas de la Atención Consciente en su formato corto (FFMQ­SF), la Entrevista de la Afectividad Adulta (AAI), así como una sesión de juego de diez minutos con sus infantes. Examinamos la frecuencia del uso de palabras por parte de los padres dentro de las categorías del programa de análisis de texto Investigación Lingüística del Conteo de Palabras (LIWC) para explorar la relación entre la atención consciente y el uso del lenguaje. Se asoció la atención consciente con el uso del lenguaje cognitivo, afectivo, perceptivo y con orientación del tiempo de la AAI. Sin embargo, menos asociaciones se identificaron entre la atención consciente y el uso del lenguaje en la sesión de juego entre progenitor e infante. Los resultados se discuten en términos de su relevancia para la atención consciente y la afectividad.

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Background: Heart failure (HF) is a primary public health issue associated with a high mortality rate. However, effective treatments still need to be developed. The optimal level of glycemic control in non-diabetic critically ill patients suffering from HF is uncertain. Therefore, this study examined the relationship between initial glucose levels and in-hospital mortality in critically ill non-diabetic patients with HF. Methods: A total of 1159 critically ill patients with HF were selected from the Medical Information Mart for Intensive Care-III (MIMIC-III) data resource and included in this study. The association between initial glucose levels and hospital mortality in seriously ill non-diabetic patients with HF was analyzed using smooth curve fittings and multivariable Cox regression. Stratified analyses were performed for age, gender, hypertension, atrial fibrillation, CHD with no MI (coronary heart disease with no myocardial infarction), renal failure, chronic obstructive pulmonary disease (COPD), estimated glomerular filtration rate (eGFR), and blood glucose concentrations. Results: The hospital mortality was identified as 14.9%. A multivariate Cox regression model, along with smooth curve fitting data, showed that the initial blood glucose demonstrated a U-shape relationship with hospitalized deaths in non-diabetic critically ill patients with HF. The turning point on the left side of the inflection point was HR 0.69, 95% CI 0.47-1.02, p = 0.068, and on the right side, HR 1.24, 95% CI 1.07-1.43, p = 0.003. Significant interactions existed for blood glucose concentrations (7-11 mmol/L) (p-value for interaction: 0.009). No other significant interactions were detected. Conclusions: This study demonstrated a U-shape correlation between initial blood glucose and hospital mortality in critically ill non-diabetic patients with HF. The optimal level of initial blood glucose for non-diabetic critically ill patients with HF was around 7 mmol/L.

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There is a broad differential for new-onset cardiac dysrhythmia, and the rapid identification of the underlying cause of these cardiac emergencies can be lifesaving. Identifying wall motion abnormalities on point-of-care ultrasound (POCUS) is not a core echocardiography application for Emergency Medicine (EM) physicians. However, ruling in a regional wall motion abnormality can expedite patient-centered care and assist the busy EM physician in high-risk cases.

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Haploinsufficiency of the nuclear receptor binding SET domain-containing protein 1 gene (NSD1) leads to a neurodevelopmental disorder known as Sotos syndrome (SOTOS). This study investigated the effects of NSD1 knockdown in glial cells. U87MG glioma cells were transfected with siRNA targeting NSD1, which resulted in morphological changes characteristic of activated astrocytes. These activated phenotypes were accompanied by specific activation of mitogen-activated protein kinase (MAPK) signaling pathways, particularly those mediated by p38 MAPK and c-Jun N-terminal kinase (JNK). Transcriptome analysis showed increased expression of proinflammatory cytokine genes, particularly interleukin (IL)-1α, IL-1ß, and IL-6, following NSD1 knockdown. Treatment with MAPK inhibitors significantly reduced the cytokine induction caused by NSD1 knockdown, with the p38 MAPK inhibitor being more effective than the JNK inhibitor. These findings provide new insights into the role of NSD1 loss in neurological dysfunctions associated with SOTOS.