Programmable chitosan-based double layer seed coating for biotic and abiotic-stress tolerance in groundnut.

In the face of agricultural challenges posed by both abiotic and biotic stressors, phytopathogens emerge as formidable threats to crop productivity. Conventional methods, involving the use of pesticides and microbes, often lead to unintended consequences. In addressing this issue, ICAR -Indian Institute of Oilseeds Research (ICAR-IIOR) has developed a chitosan-based double-layer seed coating. Emphasizing crop input compatibility, entrapment, and characterization, the study has yielded promising results. The double-layer coating on groundnut seeds enhanced germination and seedling vigor. Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) confirmed the structural changes and entrapment of crop inputs. The persistence of T. harzianum (Th4d) and Bradyrhizobium sp. in chitosan blended film in studied soils revealed that viable propogules of Th4d were recorded in double layer treatment combination with 3.54 and 3.50 Log CFUs/g of soil (colony forming units) and Bradyrhizobium sp. with 5.34 and 5.27 Log CFUs/g of soil at 90 days after application (DAA). Root colonization efficacy studies of Th4d and Bradyrhizobium sp. in groundnut crop in studied soils revealed that, maximum viable colonies were observed at 45 days after sowing (DAS). This comprehensive study highlights the potential of chitosan-based double-layer seed coating providing a promising and sustainable strategy for stress management in agriculture.

Virtual microscopy as a teaching-learning tool for histology in a competency-based medical curriculum.

Background: Histology forms an important component of first-year medical education. Unfortunately, it is limited to the practical laboratory due to the need for a microscope and good quality slides. Virtual microscopy is a recent advancement, which uses computers as an alternative to microscopes. The aim of the study was to compare virtual microscopy (VM)-based practical classes with traditional microscopy (TM)-based practical classes for two cohorts of first-year medical students, by comparing learning achieved using two different test scores as well as a qualitative assessment of student and faculty perspectives regarding the feasibility and usefulness of VM. Methods: Each cohort of students was divided into two equal batches and each batch underwent eight histology modules of which, four utilised traditional microscopes and four utilised virtual microscopes. Quantitative analysis was performed using a theory test (which assessed preparation, theory knowledge and understanding) as well as a spotter test (which assessed identification skills, reasoning, and recall). Qualitative analysis was performed using a structured questionnaire and focus group discussions. Results: Modules using VM were better when compared with those using TM, showing statistically significant and better grades. Qualitative analysis performed, yielded important information as to how this technology can serve as a good adjunct to traditional histology classes in the competency-based curriculum by increasing student interest, enabling self-study, and reducing students dependence on the tutor. Conclusions: VM forms a good adjunct as well as a standalone modality of learning to TM, as it improves accessibility to slides and promotes self-learning.

Integration of pan-cancer multi-omics data for novel mixed subgroup identification using machine learning methods.

Cancer is a heterogeneous disease, and patients with tumors from different organs can share similar epigenetic and genetic alterations. Therefore, it is crucial to identify the novel subgroups of patients with similar molecular characteristics. It is possible to propose a better treatment strategy when the heterogeneity of the patient is accounted for during subgroup identification, irrespective of the tissue of origin. This work proposes a machine learning (ML) based pipeline for subgroup identification in pan-cancer. Here, mRNA, miRNA, DNA methylation, and protein expression features from pan-cancer samples were concatenated and non-linearly projected to a lower dimension using an ML algorithm. This data was then clustered to identify multi-omics-based novel subgroups. The clinical characterization of these ML subgroups indicated significant differences in overall survival (OS) and disease-free survival (DFS) (p-value<0.0001). The subgroups formed by the patients from different tumors shared similar molecular alterations in terms of immune microenvironment, mutation profile, and enriched pathways. Further, decision-level and feature-level fused classification models were built to identify the novel subgroups for unseen samples. Additionally, the classification models were used to obtain the class labels for the validation samples, and the molecular characteristics were verified. To summarize, this work identified novel ML subgroups using multi-omics data and showed that the patients with different tumor types could be similar molecularly. We also proposed and validated the classification models for subgroup identification. The proposed classification models can be used to identify the novel multi-omics subgroups, and the molecular characteristics of each subgroup can be used to design appropriate treatment regimen.

Genome sequencing of cucumber mosaic virus (CMV) isolates infecting chilli and its interaction with host ferredoxin protein of different host for causing mosaic symptoms.

Chilli (Capsicum annuum L.) is an important vegetable crop grown in the Indian sub-continent and is prone to viral infections under field conditions. During the field survey, leaf samples from chilli plants showing typical symptoms of disease caused by cucumber mosaic virus (CMV) such as mild mosaic, mottling and leaf distortion were collected. DAC-ELISA analysis confirmed the presence of CMV in 71 out of 100 samples, indicating its widespread prevalence in the region. Five CMV isolates, named Gu1, Gu2, BA, Ho, and Sal were mechanically inoculated onto cucumber and Nicotiana glutinosa plants to study their virulence. Inoculated plants expressed the characteristic symptoms of CMV such as chlorotic spots followed by mild mosaic and leaf distortion. Complete genomes of the five CMV isolates were amplified, cloned, and sequenced, revealing RNA1, RNA2, and RNA3 sequences with 3358, 3045, and 2220 nucleotides, respectively. Phylogenetic analysis classified the isolates as belonging to the CMV-IB subgroup, distinguishing them from subgroup IA and II CMV isolates. Recombination analysis showed intra and interspecific recombination in all the three RNA segments of these isolates. In silico protein-protein docking approach was used to decipher the mechanism behind the production of mosaic symptoms during the CMV-host interaction in 13 host plants. Analysis revealed that the production of mosaic symptoms could be due to the interaction between the coat protein (CP) of CMV and chloroplast ferredoxin proteins. Further, in silico prediction was validated in 13 host plants of CMV by mechanical sap inoculation. Twelve host plants produced systemic symptoms viz., chlorotic spot, chlorotic ringspot, chlorotic local lesion, mosaic and mild mosaic and one host plant, Solanum lycopersicum produced mosaic followed by shoestring symptoms. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03777-8.

Investigation of fullerene cluster growth mechanisms by carbon atom addition using classical molecular dynamics.

The mechanisms of carbon sticking reactions to C36 and C-C80 fullerenes were investigated with molecular dynamics simulations (MD) using the Second-generation Reactive Empirical Bond Order (SREBO) and Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potentials that were specifically optimized for carbon-carbon interactions. Results showed the existence of three possible sticking configurations where the projectile atom can stick either to one, two or three atoms of the target fullerene. They also showed that although the two potentials give similar magnitudes for the sticking cross-sections, they yield fairly different results as far as sticking mechanisms and configurations at thermal collision-energies, i.e., in the range 0.05-0.5 eV, are concerned. While AIREBO, that takes into account the long-range Lennard-Jones interaction, essentially results in a surface-sticking configuration with a single atom of the target fullerene, SREBO potential yields both surface- and two neighbors-sticking (2N-sticking) configurations. The fullerene structure is preserved in the last configuration while it can be recovered by a 2000 K annealing in the former configuration. Results obtained with SREBO eventually showed larger sticking probabilities for C36 as compared with C80. In spite of this, the sticking cross-sections obtained for C80 are similar to or even larger than those obtained for C36 due to the larger size of C80 that compensates for its smaller sticking probabilities.

A Doctor in the House, An Ethical Consideration on Treating Their Family Members: A Mixed-Method Study.

BACKGROUND:  The ethical dilemma of doctors treating their own family members has long been a contentious issue in the field of medicine. Despite these dilemmas, doctors may feel compelled to become involved in the care of family members and reluctant to set standards for themselves. Therefore, this study aimed to assess the experience of doctors in the treatment of their families in Perambalur District, Tamil Nadu, India. METHODOLOGY:  A mixed-method study was conducted among the doctors in Perambalur District, Tamil Nadu, India from December 2021 to October 2022. A semi-structured questionnaire was used to assess the socio-demographic details and the experience of doctors in treating their family members, followed by a focused group discussion (FGD). Data were analyzed using SPSS version 16 (SPSS Inc., Chicago). A scatter plot was created to visualize the relationship between age, experience of doctors, and confidence level with the frequency of treating family members. A chi-square test was performed to find any associations, and a p-value <0.05 was considered significant. For qualitative data, a fish herringbone model was constructed. RESULTS:  A total of 72 doctors participated in the study. The study found that almost all the doctors (100%) received medical requests from family members, the median number of requests received in a year was 6.5 with an interquartile range of 4-8 and three-quarters (66.6%) of them accepted the requests and treated them. However, concerns about maintaining objectivity, emotional attachment, and loss of confidentiality were cited as primary reasons for not accepting all requests. The study also found a positive relationship between age, years of experience, and the frequency of treating family members. The FGD highlighted challenges related to potential risks in managing complex cases, emotional involvement impacting decision-making, conflicts of interest, and pressures from family members and societal norms. CONCLUSION:  In the present study, almost all the doctors received requests from their family members in the last year, and more than three-fourths of the doctors treated their family members. One-fourth of the doctors rejected requests from family members due to concerns about the potential loss of objectivity and the risk of misdiagnosing symptoms caused by emotional attachments. This study sheds light on the complexities and ethical considerations doctors face when treating family members. It emphasizes the need for medical ethics education in the curriculum to guide future doctors in making ethical decisions in such situations. Implementing clear-cut medical ethics guidelines in India would be instrumental in addressing these issues and ensuring ethical practices in the medical field.

Development of a highly potent and selective degrader of LRRK2.

The discovery of disease-modifying therapies for Parkinson's Disease (PD) represents a critical need in neurodegenerative medicine. Genetic mutations in leucine-rich repeat kinase 2 (LRRK2) are risk factors for the development of PD, and some of these mutations have been linked to increased LRRK2 kinase activity and neuronal toxicity in cellular and animal models. Furthermore, LRRK2 function as a scaffolding protein in several pathways has been implicated as a plausible mechanism underlying neurodegeneration caused by LRRK2 mutations. Given that both the kinase activity and scaffolding function of LRRK2 have been linked to neurodegeneration, we developed proteolysis-targeting chimeras (PROTACs) targeting LRRK2. The degrader molecule JH-XII-03-02 (6) displayed high potency and remarkable selectivity for LRKK2 when assessed in a of 468 panel kinases and serves the dual purpose of eliminating both the kinase activity as well as the scaffolding function of LRRK2.

Controlled viscous fingering in volatile fluid towards spontaneous evolution of ordered 3D patterns.

Mimicking nature using artificial technologies has always been a quest/fascination of scientists and researchers of all eras. This paper characterizes viscous fingering instability-based, lithography-less, spontaneous, and scalable process towards fabrication of 3D patterns like nature-inspired honeycomb structures with ultra-high aspect ratio walls. Rich experimental characterization data on volatile polymer solution evolution in a uniport lifted Hele-Shaw cell (ULHSC) is represented on a non-dimensional phase plot. The plot with five orders of magnitude variation of non-dimensional numbers on each axis demarcates the regions of several newly observed phenomena: 'No retention', 'Bridge breaking', and 'Wall formation' with 'stable' and 'unstable' interface evolution. A new non-dimensional ratio of the velocity of evaporating static interface versus lifting velocity is proposed for the same. This phase plot along with physical insights into the phenomena observed, pave pathways for extending the method to multiport LHSC (MLHSC) to demonstrate multiwell honeycomb structures. The work thus establishes a solid foundation with valuable insights for scalable manufacturing of devices useful for application in biomedical and other domains.

Exploring the diversity of virulence genes in the Magnaporthe population infecting millets and rice in India.

Blast pathogen, Magnaporthe spp., that infects ancient millet crops such pearl millet, finger millet, foxtail millet, barnyard millet, and rice was isolated from different locations of blast hotspots in India using single spore isolation technique and 136 pure isolates were established. Numerous growth characteristics were captured via morphogenesis analysis. Among the 10 investigated virulent genes, we could amplify MPS1 (TTK Protein Kinase) and Mlc (Myosin Regulatory Light Chain edc4) in majority of tested isolates, regardless of the crop and region where they were collected, indicating that these may be crucial for their virulence. Additionally, among the four avirulence (Avr) genes studied, Avr-Pizt had the highest frequency of occurrence, followed by Avr-Pia. It is noteworthy to mention that Avr-Pik was present in the least number of isolates (9) and was completely absent from the blast isolates from finger millet, foxtail millet, and barnyard millet. A comparison at the molecular level between virulent and avirulent isolates indicated observably large variation both across (44%) and within (56%) them. The 136 Magnaporthe spp isolates were divided into four groups using molecular markers. Regardless of their geographic distribution, host plants, or tissues affected, the data indicate that the prevalence of numerous pathotypes and virulence factors at the field level, which may lead to a high degree of pathogenic variation. This research could be used for the strategic deployment of resistant genes to develop blast disease-resistant cultivars in rice, pearl millet, finger millet, foxtail millet, and barnyard millet.

Development and validation of the interpersonal communication assessment tool for assessing the interpersonal communication skills of public health midwives.

BACKGROUND: Interpersonal Communication Skills (IPCS) are one of the core clinical skills that should be developed by the Public Health Midwives (PHMs), who are grass-root level public healthcare providers in primary healthcare settings in Sri Lanka. This study aimed to develop and validate the Interpersonal Communication Assessment Tool (IPCAT), an observational rating scale, to assess the IPCS of PHMs. METHODS: Item generation, item reduction, instrument drafting, and development of the tool's rating guide were made by an expert panel. A cross-sectional study was conducted in five randomly selected Medical Officer of Health (MOH) areas, the smallest public health administrative division in the district of Colombo, Sri Lanka, to identify the factor structure, which is the correlational relationship between a number of variables in the tool. A sample of 164 PHMs was recruited. The data on IPCS were collected by video-recording the provider-client interaction using simulated clients. All recorded videos were rated by a rater using the drafted IPCAT, which included a Likert scale of 1(poor) to 5 (excellent). Exploratory factor analysis was conducted using the Principal Axis Factoring extraction method and the Varimax rotation technique to explore the factors. Three independent raters were used to rate ten randomly selected videos to assess the tool's internal consistency and inter-rater reliability. RESULTS: The IPCAT obtained a five-factor model with 22 items, and all five factors explained 65% of the total variance. The resulting factors were "Engaging" (six items on making rapport), "Delivering" (four items on paying respect), "Questioning" (four items on asking questions), "Responding" (four items on empathy), and "Ending" (four items to assess the skills of ending a conversation productively). The internal consistency, Cronbach's Alpha value, for all five factors was above 0.8, and the inter-rater reliability (ICC) was excellent (0.95). CONCLUSIONS: The Interpersonal Communication Assessment Tool is a valid and reliable tool for assessing the interpersonal communication skills of Public Health Midwives. TRIAL REGISTRATION: Clinical Trial Registry, Sri Lanka. Ref No, SLCTR/2020/006(February 4th,2020).

Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer.

Non-small Cell Lung Cancer (NSCLC) is a heterogeneous disease with a poor prognosis. Identifying novel subtypes in cancer can help classify patients with similar molecular and clinical phenotypes. This work proposes an end-to-end pipeline for subgroup identification in NSCLC. Here, we used a machine learning (ML) based approach to compress the multi-omics NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1-C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. Decision­level fused classification models were then built using these classifiers, which were used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics.

Phenotypic and Genotypic screening of fifty-two rice (Oryza sativa L.) genotypes for desirable cultivars against blast disease.

Magnaporthe oryzae, the rice blast fungus, is one of the most dangerous rice pathogens, causing considerable crop losses around the world. In order to explore the rice blast-resistant sources, initially performed a large-scale screening of 277 rice accessions. In parallel with field evaluations, fifty-two rice accessions were genotyped for 25 major blast resistance genes utilizing functional/gene-based markers based on their reactivity against rice blast disease. According to the phenotypic examination, 29 (58%) and 22 (42%) entries were found to be highly resistant, 18 (36%) and 29 (57%) showed moderate resistance, and 05 (6%) and 01 (1%), respectively, were highly susceptible to leaf and neck blast. The genetic frequency of 25 major blast resistance genes ranged from 32 to 60%, with two genotypes having a maximum of 16 R-genes each. The 52 rice accessions were divided into two groups based on cluster and population structure analysis. The highly resistant and moderately resistant accessions are divided into different groups using the principal coordinate analysis. According to the analysis of molecular variance, the maximum diversity was found within the population, while the minimum diversity was found between the populations. Two markers (RM5647 and K39512), which correspond to the blast-resistant genes Pi36 and Pik, respectively, showed a significant association to the neck blast disease, whereas three markers (Pi2-i, Pita3, and k2167), which correspond to the blast-resistant genes Pi2, Pita/Pita2, and Pikm, respectively, showed a significant association to the leaf blast disease. The associated R-genes might be utilized in rice breeding programmes through marker-assisted breeding, and the identified resistant rice accessions could be used as prospective donors for the production of new resistant varieties in India and around the world.

Current Novel Drug Deliveries for Oral Cancer: A Chronotherapeutic Approach.

Oral squamous cell carcinoma is a malignant disease that is causing considerable mortality worldwide. Conventional treatment approaches, like surgery, cause destructive alterations in facial appearance and oral function impairments associated with psychological and social functioning. Chemotherapy exhibits low bioaccessibility of the anticancer drugs, multiple drug resistance, higher dose necessities, which elevate toxicities to the normal cells, low therapeutic index, and non-specific targeting. Radiation therapies significantly affect the well-being of the patient and impair the quality of life. Therefore, chemotherapeutics are developed that can either actively or passively target the carcinomas, reduce the adverse side effect, and improve therapeutic efficacy. Innovations in novel drug delivery systems deliver the drugs to the desired site of action with better treatment approaches with reduced toxicities to the normal cells and improve the health and survival rate of the patient. Cancer chronotherapy enhances the treatment proficiency by administration of the drugs at the best time, considering biological timings to improve the treatment profiles. Chronotherapy provides benefits to the current anticancer therapies, with minimum adverse effects to the healthy cells. This review discusses the risk factors for oral carcinomas, targeted therapy by nanocarriers, nanotechnology approaches, the role of circadian rhythm in the management of oral cancer, and advances in controlled drug delivery.

Omicron infection increases IgG binding to spike protein of predecessor variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in India in 2020-2022 was driven predominantly by Wild (Wuhan-Hu-1 and D614G), Delta, and Omicron variants. The aim of this study was to examine the effect of infections on the humoral immune response and cross-reactivity to spike proteins of Wuhan-Hu-1, Delta, C.1.2., and Omicron. Residual archival sera (N = 81) received between January 2020 and March 2022 were included. Infection status was inferred by a positive SARS-CoV-2 RT-PCR and/or serology (anti-N and anti-S antibodies) and sequencing of contemporaneous samples (N = 18) to infer lineage. We estimated the levels and cross-reactivity of infection-induced sera including Wild, Delta, Omicron as well as vaccine breakthrough infections (Delta and Omicron). We found an approximately two-fold increase in spike-specific IgG antibody binding in post-Omicron infection compared with the pre-Omicron period, whilst the change in pre- and post-Delta infections were similar. Further investigation of Omicron-specific humoral responses revealed primary Omicron infection as an inducer of cross-reactive antibodies against predecessor variants, in spite of the weaker degree of humoral response compared to Wuhan-Hu-1 and Delta infection. Intriguingly, Omicron vaccine-breakthrough infections when compared with primary infections, exhibited increased humoral responses against RBD (7.7-fold) and Trimeric S (Trimeric form of spike protein) (34.6-fold) in addition to increased binding of IgGs towards previously circulating variants (4.2 - 6.5-fold). Despite Delta breakthrough infections showing a higher level of humoral response against RBD (2.9-fold) and Trimeric S (5.7-fold) compared to primary Delta sera, a demonstrably reduced binding (36%-49%) was observed to Omicron spike protein. Omicron vaccine breakthrough infection results in increased intensity of humoral response and wider breadth of IgG binding to spike proteins of antigenically-distinct, predecessor variants.

Scalable large-area mesh-structured microfluidic gradient generator for drug testing applications.

Microfluidic concentration gradient generators are useful in drug testing, drug screening, and other cellular applications to avoid manual errors, save time, and labor. However, expensive fabrication techniques make such devices prohibitively costly. Here, in the present work, we developed a microfluidic concentration gradient generator (µCGG) using a recently proposed non-conventional photolithography-less method. In this method, ceramic suspension fluid was shaped into a square mesh by controlling Saffman Taylor instability in a multiport lifted Hele-Shaw cell (MLHSC). Using the shaped ceramic structure as the template, µCGG was prepared by soft lithography. The concentration gradient was characterized and effect of the flow rates was studied using COMSOL simulations. The simulation result was further validated by creating a fluorescein dye (fluorescein isothiocanate) gradient in the fabricated µCGG. To demonstrate the use of this device for drug testing, we created various concentrations of an anticancer drug-curcumin-using the device and determined its inhibitory concentration on cervical cancer cell-line HeLa. We found that the IC50 of curcumin for HeLa matched well with the conventional multi-well drug testing method. This method of µCGG fabrication has multiple advantages over conventional photolithography such as: (i) the channel layout and inlet-outlet arrangements can be changed by simply wiping the ceramic fluid before it solidifies, (ii) it is cost effective, (iii) large area patterning is easily achievable, and (iv) the method is scalable. This technique can be utilized to achieve a broad range of concentration gradient to be used for various biological and non-biological applications.

Under-resourced dialect identification in Ao using source information.

This paper reports the findings of an automatic dialect identification (DID) task conducted on Ao speech data using source features. Considering that Ao is a tone language, in this study for DID, the gammatonegram of the linear prediction residual is proposed as a feature. As Ao is an under-resourced language, data augmentation was carried out to increase the size of the speech corpus. The results showed that data augmentation improved DID by 14%. A perception test conducted on Ao speakers showed better DID by the subjects when utterance duration was 3 s. Accordingly, automatic DID was conducted on utterances of various duration. A baseline DID system with the Slms feature attained an average F1-score of 53.84% in a 3 s long utterance. Inclusion of source features, Silpr and S, improved the F1-score to 60.69%. In a final system, with a combination of Silpr, S, Slms, and Mel frequency cepstral coefficient features, the F1-score increased to 61.46%.

Bio-Mimicking Brain Vasculature to Investigate the Role of Heterogeneous Shear Stress in Regulating Barrier Integrity.

A continuous, sealed endothelial membrane is essential for the blood-brain barrier (BBB) to protect neurons from toxins present in systemic circulation. Endothelial cells are critical sensors of the capillary environment, where factors like fluid shear stress (FSS) and systemic signaling molecules activate intracellular pathways that either promote or disrupt the BBB. The brain vasculature exhibits complex heterogeneity across the bed, which is challenging to recapitulate in BBB microfluidic models with fixed dimensions and rectangular cross-section microchannels. Here, a Cayley-tree pattern, fabricated using lithography-less, fluid shaping technique in a modified Hele-Shaw cell is used to emulate the brain vasculature in a microfluidic chip. This geometry generates an inherent distribution of heterogeneous FSS, due to smooth variations in branch height and width. hCMEC/D3 endothelial cells cultured in the Cayley-tree designed chip generate a 3D monolayer of brain endothelium with branching hierarchy, enabling the study of the effect of heterogeneous FSS on the brain endothelium. The model is employed to study neuroinflammatory conditions by stimulating the brain endothelium with tumor necrosis factor-α under heterogeneous FSS conditions. The model has immense potential for studies involving drug transport across the BBB, which can be misrepresented in fixed dimension models.

A deep tensor-based approach for automatic depression recognition from speech utterances.

Depression is one of the significant mental health issues affecting all age groups globally. While it has been widely recognized to be one of the major disease burdens in populations, complexities in definitive diagnosis present a major challenge. Usually, trained psychologists utilize conventional methods including individualized interview assessment and manually administered PHQ-8 scoring. However, heterogeneity in symptomatic presentations, which span somatic to affective complaints, impart substantial subjectivity in its diagnosis. Diagnostic accuracy is further compounded by the cross-sectional nature of sporadic assessment methods during physician-office visits, especially since depressive symptoms/severity may evolve over time. With widespread acceptance of smart wearable devices and smartphones, passive monitoring of depression traits using behavioral signals such as speech presents a unique opportunity as companion diagnostics to assist the trained clinicians in objective assessment over time. Therefore, we propose a framework for automated depression classification leveraging alterations in speech patterns in the well documented and extensively studied DAIC-WOZ depression dataset. This novel tensor-based approach requires a substantially simpler implementation architecture and extracts discriminative features for depression recognition with high f1 score and accuracy. We posit that such algorithms, which use significantly less compute load would allow effective onboard deployment in wearables for improve diagnostics accuracy and real-time monitoring of depressive disorders.

Lineage reprogramming of human adipose mesenchymal stem cells to immune modulatory i-Heps.

Pluripotent stem cell derived-hepatocytes depict fetal -hepatocyte characteristics/maturity and are immunogenic limiting their applications. Attempts have been made to derive hepatocytes from mesenchymal stem cells using developmental cocktails, epigenetic modulators and small molecules. However, achieving a stable terminally differentiated functional state had been a challenge. Inefficient hepatic differentiation could be due to lineage restrictions set during development. Hence a novel lineage reprogramming approach has been utilized to confer competence to adipose-mesenchymal stem cells (ADMSCs) to efficiently respond to hepatogenic cues and achieve a stable functional hepatic state. Lineage reprogramming involved co-transduction of ADMSCs with hepatic endoderm pioneer Transcription factor (TF)-FOXA2, HHEX-a homeobox gene and HNF4α-master TF indispensable for hepatic state maintenance. Lineage priming was evidenced by endogenous HFN4α promoter demethylation and robust responsiveness to minimal hepatic maturation cues. Induced hepatocytes (i-Heps) exhibited mesenchymal-to-epithelial transition and terminal hepatic signatures. Functional characterisation of i-Heps for hepatic drug detoxification systems, xenobiotic uptake/clearance, metabolic status and hepatotropic virus entry validated acquisition of stable hepatic state and junctional maturity Exhaustive analysis of MSC memory in i-Heps indicated loss of MSC-immunophenotype and terminal differentiation to osteogenic/adipogenic lineages. Importantly, i-Heps suppressed phytohemagglutinin-induced T-cell blasts, inhibited allogenic mixed-lymphocyte reactions (MLRs) and secreted immunomodulatory- indoleamine 2,3-dioxygenase in T-cell blast co-cultures akin to native ADMSCs. In a nutshell, the present study identifies a novel cocktail of TFs that reprogram ADMSCs to stable hepatic state. i-Heps exhibit adult hepatocyte functional maturity with robust immune-modulatory abilities rendering suitability for rigorous drug testing, hepatocyte-pathogen interaction studies and transplantation in allogenic settings.

Selective Macrocyclic Inhibitors of DYRK1A/B.

Dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A (DYRK1A) is a therapeutic target of interest due to the roles it plays in both neurological diseases and cancer. We present the development of the first macrocyclic inhibitors of DYRK1A. Initial lead inhibitor JH-XIV-68-3 (3) displayed selectivity for DYRK1A and close family member DYRK1B in biochemical and cellular assays, and demonstrated antitumor efficacy in head and neck squamous cell carcinoma (HNSCC) cell lines. However, we noted that it suffered from rapid aldehyde oxidase (AO)-mediated metabolism. To overcome this liability, we generated a derivative (JH-XVII-10 (10)), where fluorine was introduced to block the 2-position of the azaindole and render the molecule resistant to AO activity. We showed that 10 maintains remarkable potency and selectivity in biochemical and cellular assays as well as antitumor efficacy in HNSCC cell lines and improved metabolic stability. Therefore, 10 represents a promising new scaffold for developing DYRK1A-targeting chemical probes and therapeutics.