Harmonizing immune cell sequences for computational analysis with large language models.

We present SEQuence Weighted Alignment for Sorting and Harmonization (Seqwash), an algorithm designed to process sequencing profiles utilizing large language models. Seqwash harmonizes immune cell sequences into a unified representation, empowering LLMs to embed meaningful patterns while eliminating irrelevant information. Evaluations using immune cell sequencing data showcase Seqwash's efficacy in standardizing profiles, leading to improved feature quality and enhanced performance in both supervised and unsupervised downstream tasks for sequencing data.

Oligoprotective Activity of Levetiracetam Against Glutamate Toxicity: An in vitro Study.

INTRODUCTION: The role of glutamate in the development of some brain pathological conditions, such as multiple sclerosis, has been well described. Levetiracetam (LEV), a new broad-spectrum antiseizure medicine, is widely used to control certain types of seizures. METHOD: Apart from its anti-seizure activity, LEV exerts neuroprotection via anti-inflammatory, antioxidant, and antiapoptotic effects. The current study was designed to evaluate the protective potential of LEV against glutamate-induced injury in OLN-93 oligodendrocytes. METHOD: At first, the potential negative impact of LEV on OLN-93 viability was evaluated. After that, the cells were concurrently treated with LEV (0-100 µM) and glutamate (8 mM) for 24 h. The viability, redox status, and the rate of apoptosis of OLN-93 cells were then assessed using 3-[4,5-dimethylthiazol- 2-yl]-2,5-diphenyl-2H-tetrazolium bromide (MTT), 2',7' dichlorodihydrofluorescein diacetate (H2DCFDA), 2-thiobarbituric acid reactive substances (TBARS) and annexin V/propidium iodide (PI) assays, respectively. Moreover, caspase-3 expression, as a marker of cell apoptosis, was evaluated by western blotting. RESULTS: LEV at 1-800 µM did not have any negative effect on cell survival. Treatment with LEV (50 and 100 µM) substantially enhanced the cell viability following glutamate insult. The cytoprotective activity of LEV (50 and 100 µM) against glutamate toxicity was accompanied by reduced Reactive Oxygen Species (ROS) accumulation and Malondialdehyde (MDA) level. Moreover, 100 µM of LEV inhibited apoptosis and decreased the expression level of cleaved caspase-3 following glutamate exposure. CONCLUSION: Taken together, the results suggested that LEV has protective effects against glutamate-mediated cytotoxicity in OLN-93 cells. The oligoprotective action of LEV was shown to be exerted via inhibition of oxidative stress and cellular apoptosis.

Novel crossover and recombination hotspots massively spread across primate genomes.

BACKGROUND: The recombination landscape and subsequent natural selection have vast consequences forevolution and speciation. However, most of the crossover and recombination hotspots are yet to be discovered. We previously reported the relevance of C and G trinucleotide two-repeat units (CG-TTUs) in crossovers and recombination. METHODS: On a genome-wide scale, here we mapped all combinations of A and T trinucleotide two-repeat units (AT-TTUs) in human, consisting of AATAAT, ATAATA, ATTATT, TTATTA, TATTAT, and TAATAA. We also compared a number of the colonies formed by the AT-TTUs (distance between consecutive AT-TTUs < 500 bp) in several other primates and mouse. RESULTS: We found that the majority of the AT-TTUs (> 96%) resided in approximately 1.4 million colonies, spread throughout the human genome. In comparison to the CG-TTU colonies, the AT-TTU colonies were significantly more abundant and larger in size. Pure units and overlapping units of the pure units were readily detectable in the same colonies, signifying that the units were the sites of unequal crossover. We discovered dynamic sharedness of several of the colonies across the primate species studied, which mainly reached maximum complexity and size in human. CONCLUSIONS: We report novel crossover and recombination hotspots of the finest molecular resolution, massively spread and shared across the genomes of human and several other primates. With respect to crossovers and recombination, these genomes are far more dynamic than previously envisioned.

Hyperspectral Retinal Imaging as a Non-Invasive Marker to Determine Brain Amyloid Status.

Background: As an extension of the central nervous system (CNS), the retina shares many similarities with the brain and can manifest signs of various neurological diseases, including Alzheimer's disease (AD). Objective: To investigate the retinal spectral features and develop a classification model to differentiate individuals with different brain amyloid levels. Methods: Sixty-six participants with varying brain amyloid-ß protein levels were non-invasively imaged using a hyperspectral retinal camera in the wavelength range of 450-900 nm in 5 nm steps. Multiple retina features from the central and superior views were selected and analyzed to identify their variability among individuals with different brain amyloid loads. Results: The retinal reflectance spectra in the 450-585 nm wavelengths exhibited a significant difference in individuals with increasing brain amyloid. The retinal features in the superior view showed higher inter-subject variability. A classification model was trained to differentiate individuals with varying amyloid levels using the spectra of extracted retinal features. The performance of the spectral classification model was dependent upon retinal features and showed 0.758-0.879 accuracy, 0.718-0.909 sensitivity, 0.764-0.912 specificity, and 0.745-0.891 area under curve for the right eye. Conclusions: This study highlights the spectral variation of retinal features associated with brain amyloid loads. It also demonstrates the feasibility of the retinal hyperspectral imaging technique as a potential method to identify individuals in the preclinical phase of AD as an inexpensive alternative to brain imaging.

Working Memory Capacity and Contextual Novel Linguistic Input: A Cross-Modal Priming Study on Persian-English Subordinate Bilinguals.

The present study investigated the effect of verbal working memory capacity (VWMC) on the processing of semantic information during on-line lexical ambiguity resolution of bilinguals. Seventeen Persian-English subordinate bilinguals of similar proficiency level were recruited to perform two experimental tasks: (1) a multi-load-level reading span task designed to measure their VWMC and (2) a cross-modal semantic priming task (CMPT), 24 h subsequent to the last encoding session, to assess their performance on semantic processing of L2 homographs whose subordinate readings were deemed "novel" for them. An overall 2 × 3 repeated-measures ANOVA revealed a statistically significant difference in the processing of the encoded semantic information between high and low WMC participants. The findings of the experiments lend support to the veracity of the assumptions made by Reordered Access Model in that biasing semantic context facilitates the ambiguity resolution of lexical items. Lastly, the pedagogical implications of the findings were expounded on.

Structures of the multi-domain oxygen sensor DosP: remote control of a c-di-GMP phosphodiesterase by a regulatory PAS domain.

The heme-based direct oxygen sensor DosP degrades c-di-GMP, a second messenger nearly unique to bacteria. In stationary phase Escherichia coli, DosP is the most abundant c-di-GMP phosphodiesterase. Ligation of O2 to a heme-binding PAS domain (hPAS) of the protein enhances the phosphodiesterase through an allosteric mechanism that has remained elusive. We determined six structures of full-length DosP in its aerobic or anaerobic conformations, with or without c-di-GMP. DosP is an elongated dimer with the regulatory heme and phosphodiesterase separated by nearly 180 Å. In the absence of substrate, regardless of the heme status, DosP presents an equilibrium of two distinct conformations. Binding of substrate induces DosP to adopt a single, ON-state or OFF-state conformation depending on its heme status. Structural and biochemical studies of this multi-domain sensor and its mutants provide insights into signal regulation of second-messenger levels.

Multiple ancestries and shared gene flow among modern livestock guarding dogs.

Livestock guarding dogs (LGDs) have been used to protect livestock for millennia. While previous works suggested a single origin of modern LGDs, the degree and source of shared ancestry have not been tested. To address this, we generated genome-wide SNP data from 304 LGDs and combined it with public genomic data from 2,183 modern and 22 ancient dogs. Our findings reveal shared ancestry and extensive gene flow among modern LGD breeds which we attribute to historical livestock migrations. Additionally, admixture between LGDs and free-ranging dogs argues against reproductive isolation as a core mechanism for maintaining the specialized skills of LGDs. Finally, we identify two lineages within modern LGDs and uncover multiple ancestries tracing back to distinct Eurasian ancient dogs, concordant with the absence of a single ancestor. Overall, our work explores the complex evolutionary history of LGDs, offering valuable insights into how human and livestock co-migrations shaped this functional group.

CNVDeep: deep association of copy number variants with neurocognitive disorders.

BACKGROUND: Copy number variants (CNVs) have become increasingly instrumental in understanding the etiology of all diseases and phenotypes, including Neurocognitive Disorders (NDs). Among the well-established regions associated with ND are small parts of chromosome 16 deletions (16p11.2) and chromosome 15 duplications (15q3). Various methods have been developed to identify associations between CNVs and diseases of interest. The majority of methods are based on statistical inference techniques. However, due to the multi-dimensional nature of the features of the CNVs, these methods are still immature. The other aspect is that regions discovered by different methods are large, while the causative regions may be much smaller. RESULTS: In this study, we propose a regularized deep learning model to select causal regions for the target disease. With the help of the proximal [20] gradient descent algorithm, the model utilizes the group LASSO concept and embraces a deep learning model in a sparsity framework. We perform the CNV analysis for 74,811 individuals with three types of brain disorders, autism spectrum disorder (ASD), schizophrenia (SCZ), and developmental delay (DD), and also perform cumulative analysis to discover the regions that are common among the NDs. The brain expression of genes associated with diseases has increased by an average of 20 percent, and genes with homologs in mice that cause nervous system phenotypes have increased by 18 percent (on average). The DECIPHER data source also seeks other phenotypes connected to the detected regions alongside gene ontology analysis. The target diseases are correlated with some unexplored regions, such as deletions on 1q21.1 and 1q21.2 (for ASD), deletions on 20q12 (for SCZ), and duplications on 8p23.3 (for DD). Furthermore, our method is compared with other machine learning algorithms. CONCLUSIONS: Our model effectively identifies regions associated with phenotypic traits using regularized deep learning. Rather than attempting to analyze the whole genome, CNVDeep allows us to focus only on the causative regions of disease.

The Current State of Regionalization in Otolaryngologic Specialized Tumor Care for Tumor Diagnoses.

OBJECTIVE: The current extent of otolaryngologic cancer care regionalization is unclear. This study characterizes case volume regionalization patterns for 4 distinct otolaryngologic tumors-head and neck squamous cell carcinomas (HNSCCs), thyroid cancers (TCs), vestibular schwannomas, and pituitary adenomas (PAs). METHODS: The 2010-2016 National Cancer Database was queried for patients with HNSCCs, TCs, vestibular schwannomas, and PAs. Facility geographic locations were divided into 4 geographical quadrants. High-volume facilities (HVFs) were defined as top 100 by volume facility for ≥1 pathology. RESULTS: A total of 191/1342 facilities (4.2%) were defined as an HVF. Vestibular schwannoma was the most regionalized, with 65.9% of patients treated at an HVF. Thyroid cancer (37.4%) and HNSCC (38.8%) were the least commonly treated at HVFs. Forty-one/191 (21.5%) were classified as HVFs for all 4 pathologies. Factors predictive of treatment at HVFs included age <65, higher income, and private insurance, larger tumor size, and lower American Joint Committee on Cancer stage. CONCLUSION: Over 20% of HVFs were considered high-volume for all 4 pathologies. Vestibular schwannomas were the most regionalized compared with PAs, TCs, and HSNCCs.

Sex and gender differences in cognitive resilience to aging and Alzheimer's disease.

Sex and gender-biological and social constructs-significantly impact the prevalence of protective and risk factors, influencing the burden of Alzheimer's disease (AD; amyloid beta and tau) and other pathologies (e.g., cerebrovascular disease) which ultimately shape cognitive trajectories. Understanding the interplay of these factors is central to understanding resilience and resistance mechanisms explaining maintained cognitive function and reduced pathology accumulation in aging and AD. In this narrative review, the ADDRESS! Special Interest Group (Alzheimer's Association) adopted a multidisciplinary approach to provide the foundations and recommendations for future research into sex- and gender-specific drivers of resilience, including a sex/gender-oriented review of risk factors, genetics, AD and non-AD pathologies, brain structure and function, and animal research. We urge the field to adopt a sex/gender-aware approach to resilience to advance our understanding of the intricate interplay of biological and social determinants and consider sex/gender-specific resilience throughout disease stages. HIGHLIGHTS: Sex differences in resilience to cognitive decline vary by age and cognitive status. Initial evidence supports sex-specific distinctions in brain pathology. Findings suggest sex differences in the impact of pathology on cognition. There is a sex-specific change in resilience in the transition to clinical stages. Gender and sex factors warrant study: modifiable, immune, inflammatory, and vascular.

SPLICE: Streamlining Digital Pathology Image Processing.

Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of whole-slide images (WSIs), demand is growing for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this article, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a collage of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting nonredundant representative features. We evaluated SPLICE for search and match applications, showing its improved accuracy, reduced computation time, and storage requirements compared with existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%. This reduction enables numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology.

Progress in Early Detection of HIV in Tajikistan.

HIV early detection (CD4 counts ≥350 cells/µL) is correlated with higher life expectancy among people living with HIV (PLHIV). Several factors, including physical, cultural, structural, and financial barriers, may limit early detection of HIV. This is a first-of-its-kind study on population-level differences in early detection of HIV across time within Tajikistan and any country in the Central Asia region. Utilizing the Tajikistan Ministry of Health's national HIV data (N = 10,700) spanning 2010 to 2023, we developed median regression models with the median CD4 cell count as the outcome and with the following predictors: time (years), region, age, gender, and area (urban/rural status). Individuals younger than 19 years old were detected early for HIV, whereas those older than 39 years were detected late. Females were detected earlier compared to their male counterparts regardless of region of residence. Rural populations were detected earlier in most years compared to their urban counterparts. The COVID-19 pandemic accelerated HIV early detection in 2021 but most regions have returned to near pre-pandemic levels of detection in 2022 and 2023. There were differences identified among different demographic and geographic groups which warrant further attention.

The McCusker Subjective Cognitive Impairment Inventory (McSCI): a novel measure of perceived cognitive decline.

BACKGROUND: Subjective cognitive decline (SCD), i.e. self/other-reported concerns on one's cognitive functioning without objective evidence of significant decline, is an indicator of dementia risk. There is little consensus on reliability and validity of the available SCD measures. Therefore, introducing a novel and psychometrically sound measure of SCD is timely. OBJECTIVE: The psychometric properties of a new SCD measure, the McCusker Subjective Cognitive Impairment Inventory-Self-Report (McSCI-S), are reported. METHODS: Through review of previously published measures as well as our clinical and research data on people with SCD, we developed a 46-item self-report questionnaire to assess concerns on six cognitive domains, namely, memory, language, orientation, attention and concentration, visuoconstruction abilities and executive function. The McSCI-S was examined in a cohort of 526 participants using factor analysis, item response theory analysis and receiver operating characteristic (ROC) curve. RESULTS: A unidimensional model provided acceptable fit (CFI = 0.94, TLI = 0.94, RMSEA [90% CI] = 0.052 [.049, 0.055], WRMR = 1.45). The McSCI-S internal consistency was excellent (.96). A cut-off score of ≥24 is proposed to identify participants with SCDs. Higher McSCI-S scores were associated with poorer general cognition, episodic verbal memory, executive function and greater memory complaints and depressive scores (P < .001), controlling for age, sex and education. CONCLUSIONS: Excellent reliability and construct validity suggest the McSCI-S estimates SCDs with acceptable accuracy while capturing self-reported concerns for various cognitive domains. The psychometric analysis indicated that this measure can be used in cohort studies as well as on individual, clinical settings to assess SCDs.

DeepDRA: Drug repurposing using multi-omics data integration with autoencoders.

Cancer treatment has become one of the biggest challenges in the world today. Different treatments are used against cancer; drug-based treatments have shown better results. On the other hand, designing new drugs for cancer is costly and time-consuming. Some computational methods, such as machine learning and deep learning, have been suggested to solve these challenges using drug repurposing. Despite the promise of classical machine-learning methods in repurposing cancer drugs and predicting responses, deep-learning methods performed better. This study aims to develop a deep-learning model that predicts cancer drug response based on multi-omics data, drug descriptors, and drug fingerprints and facilitates the repurposing of drugs based on those responses. To reduce multi-omics data's dimensionality, we use autoencoders. As a multi-task learning model, autoencoders are connected to MLPs. We extensively tested our model using three primary datasets: GDSC, CTRP, and CCLE to determine its efficacy. In multiple experiments, our model consistently outperforms existing state-of-the-art methods. Compared to state-of-the-art models, our model achieves an impressive AUPRC of 0.99. Furthermore, in a cross-dataset evaluation, where the model is trained on GDSC and tested on CCLE, it surpasses the performance of three previous works, achieving an AUPRC of 0.72. In conclusion, we presented a deep learning model that outperforms the current state-of-the-art regarding generalization. Using this model, we could assess drug responses and explore drug repurposing, leading to the discovery of novel cancer drugs. Our study highlights the potential for advanced deep learning to advance cancer therapeutic precision.

The Relationship between Diet, Depression, and Alzheimer's Disease: A Narrative Review.

PURPOSE OF REVIEW: This narrative review evaluates the role of diet in the relationship between depression and Alzheimer's disease (AD). RECENT FINDINGS: AD and depression are often comorbid, and depression appears to independently increase the future risk of AD. Evidence suggests diet influences the risk of both conditions directly and indirectly. Diet impacts neurochemical and biological processes that may affect the development and progression of depression and cognitive dysfunction. The dietary components offering the greatest protection against depression and AD are yet to be determined. Current evidence highlights the importance of polyphenolic compounds, folate, B vitamins, and polyunsaturated fatty acids, along with adherence to dietary patterns like the Mediterranean diet, which includes multiple beneficial dietary factors. SUMMARY: The investigation of dietary factors in the prevention of depression and AD is a comparatively young field of research. Comprehensive highly characterised longitudinal datasets and advanced analytical approaches are required to further examine the complex relationship between diet, depression, and AD. There is a critical need for more research in this area to develop effective preventive strategies aimed at maintaining mental and physical health with advancing age.

HGTDR: Advancing drug repurposing with heterogeneous graph transformers.

MOTIVATION: Drug repurposing is a viable solution for reducing the time and cost associated with drug development. However, thus far, the proposed drug repurposing approaches still need to meet expectations. Therefore, it is crucial to offer a systematic approach for drug repurposing to achieve cost savings and enhance human lives. In recent years, using biological network-based methods for drug repurposing has generated promising results. Nevertheless, these methods have limitations. Primarily, the scope of these methods is generally limited concerning the size and variety of data they can effectively handle. Another issue arises from the treatment of heterogeneous data, which needs to be addressed or converted into homogeneous data, leading to a loss of information. A significant drawback is that most of these approaches lack end-to-end functionality, necessitating manual implementation and expert knowledge in certain stages. RESULTS: We propose a new solution, Heterogeneous Graph Transformer for Drug Repurposing (HGTDR), to address the challenges associated with drug repurposing. HGTDR is a three-step approach for knowledge graph-based drug repurposing: (1) constructing a heterogeneous knowledge graph, (2) utilizing a heterogeneous graph transformer network, and (3) computing relationship scores using a fully connected network. By leveraging HGTDR, users gain the ability to manipulate input graphs, extract information from diverse entities, and obtain their desired output. In the evaluation step, we demonstrate that HGTDR performs comparably to previous methods. Furthermore, we review medical studies to validate our method's top 10 drug repurposing suggestions, which have exhibited promising results. We also demonstrated HGTDR's capability to predict other types of relations through numerical and experimental validation, such as drug-protein and disease-protein inter-relations. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/bcb-sut/HGTDR and http://git.dml.ir/BCB/HGTDR.

Functionally distinct pericyte subsets differently regulate amyloid-ß deposition in patients with Alzheimer's disease.

Although the concept that the blood-brain barrier (BBB) plays an important role in the etiology and pathogenesis of Alzheimer's disease (AD) has become increasingly accepted, little is known yet about how it actually contributes. We and others have recently identified a novel functionally distinct subset of BBB pericytes (PCs). In the present study, we sought to determine whether these PC subsets differentially contribute to AD-associated pathologies by immunohistochemistry and amyloid beta (Aß) peptidomics. We demonstrated that a disease-associated PC subset (PC2) expanded in AD patients compared to age-matched, cognitively unimpaired controls. Surprisingly, we found that this increase in the percentage of PC2 (%PC2) was correlated negatively with BBB breakdown in AD patients, unlike in natural aging or other reported disease conditions. The higher %PC2 in AD patients was also correlated with a lower Aß42 plaque load and a lower Aß42:Aß40 ratio in the brain as determined by immunohistochemistry. Colocalization analysis of multicolor confocal immunofluorescence microscopy images suggests that AD patient with low %PC2 have higher BBB breakdown due to internalization of Aß42 by the physiologically normal PC subset (PC1) and their concomitant cell death leading to more vessels without PCs and increased plaque load. On the contrary, it appears that PC2 can secrete cathepsin D to cleave and degrade Aß built up outside of PC2 into more soluble forms, ultimately contributing to less BBB breakdown and reducing Aß plaque load. Collectively our data shows functionally distinct mechanisms for PC1 and PC2 in high Aß conditions, demonstrating the importance of correctly identifying these populations when investigating the contribution of neurovascular dysfunction to AD pathogenesis.