Vibrational spectroscopy of interstellar PAHs: 1-cyanonaphthalene and 2-cyanonaphthalene.

In this work, we present a comprehensive experimental and theoretical study of the vibrational spectra of PAH molecules recently detected in the interstellar medium: 1-cyanonaphthalene and 2-cyanonaphthalene. The room temperature IR spectra of 1- and 2-cyanonaphthalene in the region 100-3100 cm-1 and their vibrational Raman spectra in the region 35-3100 cm-1 are reported here for the first time. A detailed spectral analysis is carried out using quantum chemical calculations employing the DFT methodology. Anharmonic corrections using the VPT2 method yield excellent agreement with the experimental spectra. A re-investigation of the vibrational spectrum of the parent molecule: naphthalene validates the experimental and theoretical methods used. A consistent set of assignments is reported for the fundamental bands of 1- and 2-cyanonapththalene. The experimental and theoretical data presented here would be useful inputs for modelling the role of cyanonaphthalene in astrophysical processes.

Utility of galactomannan diagnostic assay in rhino-orbito-cerebral mycosis in COVID-19 patients.

An increasing number of fungal infections were reported post COVID-19 and many of them were caused by fungi of mixed aetiologies. This study was carried out to assess the utility of serum galactomannan (GM) assay in establishing the etiology of acute rhino-orbito-cerebral mycosis caused by Aspergillus spp. Two serum samples were obtained from 41 suspected post COVID-19 rhino-orbito-cerebral mycosis patients to perform GM assay. Serum GM assay was positive in 68.7% of the cases of proven aspergillosis at cut off OD = 1.0. Serum GM assay can be used as a supplementary test in the diagnosis of rhino-orbito-cerebral mycosis caused by Aspergillus spp.

Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma.

The poor prognosis of glioma patients brought attention to the need for effective therapeutic approaches for precision therapy. Here, we deployed algorithms relying on network medicine and artificial intelligence to design the framework for subtype-specific target identification and drug response prediction in glioma. We identified the driver mutations that were differentially expressed in each subtype of lower-grade glioma and glioblastoma multiforme and were linked to cancer-specific processes. Driver mutations that were differentially expressed were also subjected to subtype-specific disease module identification. The drugs from the drug bank database were retrieved to target these disease modules. However, the efficacy of anticancer drugs depends on the molecular profile of the cancer and varies among cancer patients due to intratumor heterogeneity. Hence, we developed a deep-learning-based drug response prediction framework using the experimental drug screening data. Models for 30 drugs that can target the disease module were developed, where drug response measured by IC50 was considered a response and gene expression and mutation data were considered predictor variables. The model construction consists of three steps: feature selection, data integration, and classification. We observed the consistent performance of the models in training, test, and validation datasets. Drug responses were predicted for particular cell lines derived from distinct subtypes of gliomas. We found that subtypes of gliomas respond differently to the drug, highlighting the importance of subtype-specific drug response prediction. Therefore, the development of personalized therapy by integrating network medicine and a deep learning-based approach can lead to cancer-specific treatment and improved patient care.

Correlation of severity & clinical outcomes of COVID-19 with virus variants: A prospective, multicentre hospital network study.

BACKGROUND OBJECTIVES: The clinical course of COVID-19 and its prognosis are influenced by both viral and host factors. The objectives of this study were to develop a nationwide platform to investigate the molecular epidemiology of SARS-CoV-2 (Severe acute respiratory syndrome Corona virus 2) and correlate the severity and clinical outcomes of COVID-19 with virus variants. METHODS: A nationwide, longitudinal, prospective cohort study was conducted from September 2021 to December 2022 at 14 hospitals across the country that were linked to a viral sequencing laboratory under the Indian SARS-CoV-2 Genomics Consortium. All participants (18 yr and above) who attended the hospital with a suspicion of SARS-CoV-2 infection and tested positive by the reverse transcription-PCR method were included. The participant population consisted of both hospitalized as well as outpatients. Their clinical course and outcomes were studied prospectively. Nasopharyngeal samples collected were subjected to whole genome sequencing to detect SARS-CoV-2 variants. RESULTS: Of the 4972 participants enrolled, 3397 provided samples for viral sequencing and 2723 samples were successfully sequenced. From this, the evolution of virus variants of concern including Omicron subvariants which emerged over time was observed and the same reported here. The mean age of the study participants was 41 yr and overall 49.3 per cent were female. The common symptoms were fever and cough and 32.5 per cent had comorbidities. Infection with the Delta variant evidently increased the risk of severe COVID-19 (adjusted odds ratio: 2.53, 95% confidence interval: 1.52, 4.2), while Omicron was milder independent of vaccination status. The independent risk factors for mortality were age >65 yr, presence of comorbidities and no vaccination. INTERPRETATION CONCLUSIONS: The authors believe that this is a first-of-its-kind study in the country that provides real-time data of virus evolution from a pan-India network of hospitals closely linked to the genome sequencing laboratories. The severity of COVID-19 could be correlated with virus variants with Omicron being the milder variant.

DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping.

BACKGROUND AND OBJECTIVE: The classification of glioma subtypes is essential for precision therapy. Due to the heterogeneity of gliomas, the subtype-specific molecular pattern can be captured by integrating and analyzing high-throughput omics data from different genomic layers. The development of a deep-learning framework enables the integration of multi-omics data to classify the glioma subtypes to support the clinical diagnosis. RESULTS: Transcriptome and methylome data of glioma patients were preprocessed, and differentially expressed features from both datasets were identified. Subsequently, a Cox regression analysis determined genes and CpGs associated with survival. Gene set enrichment analysis was carried out to examine the biological significance of the features. Further, we identified CpG and gene pairs by mapping them in the promoter region of corresponding genes. The methylation and gene expression levels of these CpGs and genes were embedded in a lower-dimensional space with an autoencoder. Next, ANN and CNN were used to classify subtypes using the latent features from embedding space. CNN performs better than ANN for subtyping lower-grade gliomas (LGG) and glioblastoma multiforme (GBM). The subtyping accuracy of CNN was 98.03% (± 0.06) and 94.07% (± 0.01) in LGG and GBM, respectively. The precision of the models was 97.67% in LGG and 90.40% in GBM. The model sensitivity was 96.96% in LGG and 91.18% in GBM. Additionally, we observed the superior performance of CNN with external datasets. The genes and CpGs pairs used to develop the model showed better performance than the random CpGs-gene pairs, preprocessed data, and single omics data. CONCLUSIONS: The current study showed that a novel feature selection and data integration strategy led to the development of DeepAutoGlioma, an effective framework for diagnosing glioma subtypes.

Conformational effects in the vibrational and electronic spectra of propionaldehyde: Experimental and theoretical studies.

We report here investigations on conformational effects in the vibrational and electronic spectra of the propionaldehyde (propanal) molecule using FTIR (600-3200 cm-1) and vacuum ultraviolet (VUV) synchrotron radiation photoabsorption (52 500-85 000 cm-1) spectroscopy respectively. Detailed theoretical calculations (using DFT and TDDFT methodologies) on ground and excited states of the cis and gauche conformers of propanal are performed; a comprehensive spectral analysis of the IR and VUV spectra is presented. A reinvestigation of the IR spectrum reveals several new bands assigned to the gauche conformer based on theoretical calculations. The VUV spectrum exhibits rich Rydberg series structure assigned to ns, np and nd series converging to the first ionization potentials of the two conformers. Earlier assignments of the 3s cis and gauche origins are revised in addition to extending Rydberg series analysis to several higher members. Vibronic bands accompanying the 3s, 4s and 4p Rydberg states are assigned using estimated vibrational frequencies of cis and gauche conformers in the cationic ground state. Simulated potential energy curves of the first few excited states (singlets and triplets) of cis and gauche conformers of propanal help in gaining insights into photodissociation mechanisms and possible conformational effects therein.

A critical appraisal of mucormycosis in COVID- 19 patients in a tertiary care centre in India.

Background and Purpose: Morbidity and mortality of opportunistic fungal infections in COVID-19 patients are less studied and defined. The patients receiving immunosuppressive therapy, broad-spectrum antibiotics, corticosteroids, and invasive and non-invasive ventilation are the high-risk groups. Materials and Methods: The demographic profile as well as clinical and radiological findings of all the patients with COVID-19 suspected of Mucormycosis (MM) were recorded. The tissue samples from all the patients were sent for microbiological (KOH mount and culture) and histopathological analysis for confirmation of MM. Results: In total, 45 COVID-19 patients suspected of MM were included in the study and MM was confirmed in 42 patients. The mean age of the patients was 50.30±14.17 years with a female: male ratio of 1.1:1. The most common symptom was headache (52.38%) followed by purulent nasal discharge (38.09%) and facial pain in 33.33% of the cases. The ocular symptoms included a diminution of vision (33.33%) and redness of the eye (2.38%). The most common site of involvement was rhino-orbital (42.85%) followed by sinonasal (23.80%) and rhino cerebral (19.04%). Majority (38.09%) of the patients were diagnosed with stage II of Rhino-orbital-cerebral Mucormycosis (ROCM) based on radiology. A history of diabetes mellitus and steroids was present in 97.61% and 85.71% of the cases, respectively. Moreover, KOH was positive for MM in 97.61% of the cases while the culture was positive in only 35.71% of the cases. In addition, on histopathology, MM was confirmed in 64.28 % of the cases. Mixed growth with Aspergillus species and Rhizopus species was observed in 14.28% of the cases in culture and 11.90% of the cases in histopathology test. Furthermore, angioinvasion was found in 23.80% of the cases according to the histopathology test. Conclusion: Based on the results, the most common conditions associated with MM in COVID-19 patients were diabetes mellitus and steroid therapy. A high level of clinical suspicion aided with diagnostic tests, including KOH mount, culture, histopathology, and radiology which helped the early detection of opportunistic fungal infection in COVID-19 patients to ensure timely treatment.

Post-COVID-19 rhino-orbito-cerebral mucormycosis-A clinico-mycological study from North India.

The second wave of coronavirus disease 2019 (COVID-19), during the early 2021, lead to a devastating outbreak of mucormycosis in India. This study aimed to determine the aetiology, clinical features, comorbidities, and risk factors of rhino-orbito-cerebral mucormycosis (ROCM) and antifungal susceptibility pattern for the isolates. The study included all suspected cases of ROCM in post-COVID-19 patients attending the hospital from May to December 2021. A total of 70 patients were diagnosed with mucormycosis during the study period. The commonest presentations were rhino-orbital and rhino-orbito-cerebral in 35.7% of cases each. Diabetes mellitus was the commonest associated risk factor in 95.7% of all patients, while 78.5% of the patients were treated with corticosteroids in the recent past, and 25.7% presented with active COVID-19 pneumonia. The commonest isolate was Rhizopus arrhizus n = 14, followed by Aspergillus flavus n = 16, A. fumigatus n = 4, A. niger n = 3, Fusarium oxysporumn = 1, and Apophysomyces variabilisn = 1. Fungal species identification was done by phenotypic methods for all the isolates and DNA sequence analysis of 18 isolates, and antifungal susceptibility testing of 30 isolates was performed by commercially prepared HiMIC plate (HiMedia, Mumbai, India) using broth microdilution for amphotericin B, isavuconazole, itraconazole, voriconazole, and posaconazole. The MIC50 and MIC90 of amphotericin B for R. arrhizus strains were 0.25 and 4 µg/ml, respectively; and the MIC50 and MIC90 results for itraconazole, posaconazole, and isavuconazole were 8 and 8, 2 and 2, and 2 and 8 µg/ml, respectively. In vitro data showed that amphotericin B was the most effective antifungal against most species. The commercially available ready-to-use minimum inhibitory concentration plates are user-friendly for performing antifungal susceptibility, which may be useful in choosing appropriate regimens and monitoring emerging resistance.

Trends of humoral immune responses to heterologous antigenic exposure due to vaccination & omicron SARS-CoV-2 infection: Implications for boosting.

Background & objectives: Vaccination and natural infection can both augment the immune responses against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but how omicron infection has affected the vaccine-induced and hybrid immunity is not well studied in Indian population. The present study was aimed to assess the durability and change in responses of humoral immunity with age, prior natural infection, vaccine type and duration with a minimum gap of six months post-two doses with either ChAdOx1 nCov-19 or BBV152 prior- and post-emergence of the omicron variant. Methods: A total of 1300 participants were included in this observational study between November 2021 and May 2022. Participants had completed at least six months after vaccination (2 doses) with either ChAdOx1 nCoV-19 or an inactivated whole virus vaccine BBV152. They were grouped according to their age (≤ or ≥60 yr) and prior exposure of SARS-CoV-2 infection. Five hundred and sixteen of these participants were followed up after emergence of the Omicron variant. The main outcome was durability and augmentation of the humoral immune response as determined by anti-receptor-binding domain (RBD) immunoglobulin G (IgG) concentrations, anti-nucleocapsid antibodies and anti-omicron RBD antibodies. Live virus neutralization assay was conducted for neutralizing antibodies against four variants - ancestral, delta and omicron and omicron sublineage BA.5. Results: Before the omicron surge, serum anti-RBD IgG antibodies were detected in 87 per cent participants after a median gap of eight months from the second vaccine dose, with a median titre of 114 [interquartile range (IQR) 32, 302] BAU/ml. The levels increased to 594 (252, 1230) BAU/ml post-omicron surge (P<0.001) with 97 per cent participants having detectable antibodies, although only 40 had symptomatic infection during the omicron surge irrespective of vaccine type and previous history of infection. Those with prior natural infection and vaccination had higher anti-RBD IgG titre at baseline, which increased further [352 (IQR 131, 869) to 816 (IQR 383, 2001) BAU/ml] (P<0.001). The antibody levels remained elevated after a mean time gap of 10 months, although there was a decline of 41 per cent. The geometric mean titre was 452.54, 172.80, 83.1 and 76.99 against the ancestral, delta, omicron and omicron BA.5 variants in the live virus neutralization assay. Interpretation & conclusions: Anti-RBD IgG antibodies were detected in 85 per cent of participants after a median gap of eight months following the second vaccine dose. Omicron infection probably resulted in a substantial proportion of asymptomatic infection in the first four months in our study population and boosted the vaccine-induced humoral immune response, which declined but still remained durable over 10 months.

Patterns and predictors of female sexual dysfunction in diabetes mellitus.

Background Sexual complications of people with diabetes mellitus (DM) are often neglected by the patients as well as clinicians. The neglect is more in women due to the associated stigma and taboo. Indian studies are scanty, varied and inconsistent, regarding the impact of DM on sexual functioning in women. We studied the patterns and predictors of sexual dysfunction in women with DM. Methods We did a cross-sectional questionnaire-based study comprising 50 participants in both the study (women with DM1 and DM2) and control groups (relatives/caregivers of patients and the hospital staff), selected randomly from the medicine outpatient department from May to August 2016. Approval from the institutional ethics committee was obtained. Clinical anxiety and depression were screened using the hospital anxiety and depression scale. Sexual dysfunction was assessed through female sexual function index scale (FSFI), and predictors were assessed by correlating FSFI scores with sociodemographic and clinical parameters. Results We found that 44% of women with DM had sexual dysfunction compared with 20% in the control group (p<0.01). The pattern of sexual dysfunction was seen across the domains of desire, arousal, lubrication and orgasm. High body mass index, higher age, duration of DM, treatment with insulin and complications of DM predicted a greater degree of sexual dysfunction among women. Conclusion Sexual dysfunction is common in women with DM, irrespective of the type of DM and coexisting psychological factors such as depression and anxiety.

Invasive Mucormycosis and Aspergillosis Coinfection Associated with Post-COVID-19 Pneumonia in a Tertiary Care Hospital.

Acute invasive fungal rhinosinusitis is a rare infection primarily affecting patients with co-morbidities like immunosuppression and poorly controlled diabetes. Mucormycosis is increasingly being reported in patients with SARS-CoV-2 (COVID-19). However, reports of coinfection of aspergillosis and mucormycosis involving nose, paranasal sinuses, orbit, and brain are rare in literature. We aimed to evaluate the patient demographics, clinical presentation, and management of cases presenting with mixed infection. We carried out retrospective analysis of 12 patients with confirmed diagnosis of mixed invasive fungal infections post-COVID-19 disease out of 70 cases of COVID-19-associated mucormycosis (CAM) presenting to a tertiary-level hospital in North India from May to June 2021. All patients had diabetes mellitus; the mean age was 48 years. The common presenting features were headache, nasal congestion, palatal ulcer, and vision loss accompanied by facial pain and swelling. Two patients developed cerebral abscess during the course of treatment; three patients had concurrent COVID-19 pneumonia. All patients received systemic liposomal amphotericin B and serial surgical debridements. The overall mortality rate was 16.7%. Our study demonstrates that mucormycosis and aspergillosis are angioinvasive mycoses that are clinically and radiologically identical. KOH direct mount of clinical sample showing septate hyphae should be extensively searched for aseptate hyphae after digestion and clearing of the tissue. A high index of suspicion of mixed infection post-COVID-19 and early initiation of liposomal amphotericin B followed by prompt surgical intervention can reduce the overall morbidity and mortality among patients with this condition.

Subtyping and grading of lower-grade gliomas using integrated feature selection and support vector machine.

Classifying lower-grade gliomas (LGGs) is a crucial step for accurate therapeutic intervention. The histopathological classification of various subtypes of LGG, including astrocytoma, oligodendroglioma and oligoastrocytoma, suffers from intraobserver and interobserver variability leading to inaccurate classification and greater risk to patient health. We designed an efficient machine learning-based classification framework to diagnose LGG subtypes and grades using transcriptome data. First, we developed an integrated feature selection method based on correlation and support vector machine (SVM) recursive feature elimination. Then, implementation of the SVM classifier achieved superior accuracy compared with other machine learning frameworks. Most importantly, we found that the accuracy of subtype classification is always high (>90%) in a specific grade rather than in mixed grade (~80%) cancer. Differential co-expression analysis revealed higher heterogeneity in mixed grade cancer, resulting in reduced prediction accuracy. Our findings suggest that it is necessary to identify cancer grades and subtypes to attain a higher classification accuracy. Our six-class classification model efficiently predicts the grades and subtypes with an average accuracy of 91% (±0.02). Furthermore, we identify several predictive biomarkers using co-expression, gene set enrichment and survival analysis, indicating our framework is biologically interpretable and can potentially support the clinician.

Gustatory dysfunction and oral ulceration in COVID-19 patients: A cross sectional study.

Background: Over the past year, patients infected by severe acute respiratory syndrome coronavirus-2 presented with severe gustatory dysfunction, the prevalence of which varies among different populations. Furthermore, there have been sporadic reports of oral ulceration observed in coronavirus disease-19 (COVID-19) patients due to varied reasons. The aim of this study was to investigate and characterize the presence of gustatory disorders, oral ulceration, and other oral changes in patients with laboratory-confirmed COVID-19 infection. Materials and Methods: In this cross-sectional observational study, a total of 402 participants who were detected COVID-19 positive by reverse transcription-polymerase chain reaction were included. Their demographic and clinical data were recorded through hospital records. The participants were interviewed either in person or on the telephone to record any change in taste and/or changes within the oral cavity. t-test for independent means was used to compare mean age, while other characteristics were compared by Chi-square test and Z-score test. P < 0.05 was taken as significant. Results: Out of the total sample of 402 individuals, 262 were male and 140 were female. The prevalence of gustatory dysfunction and oral ulceration was 43.53% and 15.67%, respectively, in the studied sample. Significantly more females had gustatory dysfunction than males and older subjects more commonly than younger. The symptom of loss/change of taste and oral ulceration were more probable to occur together. In addition, the tongue was the most common site for ulceration in our studied sample. Conclusion: Loss of taste is a common symptom of COVID-19 patients, whereas oral ulceration is not so commonly reported. However, the presence of both these symptoms could impair the quality of life of patients and hamper adequate nutritional uptake.

A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes.

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional machine learning algorithms. Furthermore, to evaluate the biological and clinical applicability of the classification, we performed weighted gene correlation network analysis, gene set enrichment, and survival analysis of the feature genes. We identified the genotype-phenotype relationship of GBM subtypes and the subtype-specific predictive biomarkers for potential diagnosis and treatment.

Resolution of viral load in mild COVID-19 patients is associated with both innate and adaptive immune responses.

Over 90% of the COVID-19 patients manifest mild/moderate symptoms or are asymptomatic. Although comorbidities and dysregulation of immune response have been implicated in severe COVID-19, the host factors that associate with asymptomatic or mild infections have not been characterized. We have collected serial samples from 23 hospitalized COVID-19 patients with mild symptoms and measured the kinetics of SARS-CoV-2 viral load in respiratory samples and markers of inflammation in serum samples. We monitored seroconversion during the acute phase of illness and quantitated the amount of total IgG against the receptor-binding domain of SARS-CoV-2 and estimated the virus neutralization potential of these antibodies. Viral load decreased by day 8 in all the patients but the detection of viral RNA in saliva samples did not correlate well with viral RNA detection in nasopharyngeal/oropharyngeal swab samples. 25% of the virus-positive patients had no detectable neutralizing antibodies in the serum and in other cases, the efficiency of antibodies to neutralize SARS-CoV-2 B1.1.7 strain was lower as compared to the circulating virus isolate. Decrease in viral load coincided with increase in neutralizing antibodies and interferon levels in serum. Most patients showed no increase in inflammatory cytokines such as IL-1ß or IL-6, however, elevated levels of IL-7 and other inflammatory mediators such as TNF-α and IL-8 was observed. These data suggest that most mild infections are associated with absence of inflammation coupled with an active innate immune response, T-cell activation and neutralizing antibodies.

Lung disease network reveals impact of comorbidity on SARS-CoV-2 infection and opportunities of drug repurposing.

BACKGROUND: Higher mortality of COVID-19 patients with lung disease is a formidable challenge for the health care system. Genetic association between COVID-19 and various lung disorders must be understood to comprehend the molecular basis of comorbidity and accelerate drug development. METHODS: Lungs tissue-specific neighborhood network of human targets of SARS-CoV-2 was constructed. This network was integrated with lung diseases to build a disease-gene and disease-disease association network. Network-based toolset was used to identify the overlapping disease modules and drug targets. The functional protein modules were identified using community detection algorithms and biological processes, and pathway enrichment analysis. RESULTS: In total, 141 lung diseases were linked to a neighborhood network of SARS-CoV-2 targets, and 59 lung diseases were found to be topologically overlapped with the COVID-19 module. Topological overlap with various lung disorders allows repurposing of drugs used for these disorders to hit the closely associated COVID-19 module. Further analysis showed that functional protein-protein interaction modules in the lungs, substantially hijacked by SARS-CoV-2, are connected to several lung disorders. FDA-approved targets in the hijacked protein modules were identified and that can be hit by exiting drugs to rescue these modules from virus possession. CONCLUSION: Lung diseases are clustered with COVID-19 in the same network vicinity, indicating the potential threat for patients with respiratory diseases after SARS-CoV-2 infection. Pathobiological similarities between lung diseases and COVID-19 and clinical evidence suggest that shared molecular features are the probable reason for comorbidity. Network-based drug repurposing approaches can be applied to improve the clinical conditions of COVID-19 patients.

Oxidative stress specifically inhibits replication of dengue virus.

Reactive oxygen species (ROS) are chemically active species which are involved in maintaining cellular and signalling processes at physiological concentrations. Therefore, cellular components that regulate redox balance are likely to play a crucial role in viral life-cycle either as promoters of viral replication or with antiviral functions. Zinc is an essential micronutrient associated with anti-oxidative systems and helps in maintaining a balanced cellular redox state. Here, we show that zinc chelation leads to induction of reactive oxygen species (ROS) in epithelial cells and addition of zinc restores ROS levels to basal state. Addition of ROS (H2O2) inhibited dengue virus (DENV) infection in a dose-dependent manner indicating that oxidative stress has adverse effects on DENV infection. ROS affects early stages of DENV replication as observed by quantitation of positive and negative strand viral RNA. We observed that addition of ROS specifically affected viral titres of positive strand RNA viruses. We further demonstrate that ROS specifically altered SEC31A expression at the ER suggesting a role for SEC31A-mediated pathways in the life-cycle of positive strand RNA viruses and provides an opportunity to identify drug targets regulating oxidative stress responses for antiviral development.

Neem (Azadirachta Indica A. Juss) Capsules for Prophylaxis of COVID-19 Infection: A Pilot, Double-Blind, Randomized Controlled Trial.

CONTEXT: SARS-CoV-2 is a global public-health concern. Interventions to prevent infection are urgently needed. The anti-inflammatory and antiviral effects of neem make it a potential agent for COVID-19 prophylaxis. OBJECTIVE: The study intended to evaluate the prophylactic effects of neem capsules for persons at high risk of COVID-19 infection due to contact with COVID-19 positive patients. DESIGN: The research team designed a prospective, randomized, double-blind, placebo-controlled, parallel-design study. SETTING: The study was conducted at a single center in India. PARTICIPANTS: Participants were 190 healthcare workers at the hospital or relatives of patients with COVID-19 infection. INTERVENTION: Of the 190 participants, 95 were in the intervention group and 95 in the control group. Participants received 50 mg of a proprietary, patent-pending, neem-leaf extract or a placebo orally in capsules, twice a day for 28 days. OUTCOME MEASURES: The number of individuals positive for COVID-19 between baseline and follow-up on day 56 was the primary outcome measure. Secondary measures included an evaluation of neem's safety and its effects on quality of life (QOL) and changes in biomarkers. RESULTS: The mean age of participants was 36.97 years, and 68.42% were male. Total 13 subjects tested positive during the study. All were asymptomatic. Of the 154 participants who completed the study per-protocol, 11 tested positive, 3 in the intervention group and 8 in the control group. The probability of COVID-19 infection in participants receiving the intervention was 0.45 times that of participants receiving the placebo, a relative risk of 0.45, with the effectiveness of the intervention being around 55%. Treatment-emergent adverse events (TEAEs) in both groups were minimal and were of grade 1 or 2 in severity. Biomarkers and QOL remained stable in both groups. CONCLUSIONS: The study found a reduced risk of COVID-19 infection in participants receiving neem capsules, which demonstrates its potential as a prophylactic treatment for the prevention of COVID-19 infection. The findings warrant further investigation in clinical trials.

Antibody-based biosensor to detect oncogenic splicing factor Sam68 for the diagnosis of lung cancer.

OBJECTIVE: The present work aimed to investigate the potential utility of Sam68 protein as a prognostic marker in lung cancer. Then an electrochemical immunosensor is fabricated that is sufficiently sensitive to detect Sam68. RESULTS: Analysis of stage-specific Lung cancer microarray data shows that differential expression of Sam68 is associated with cancer stage and monotonically increases from early tumor stage to advanced metastatic stage. Moreover, the higher expression of Sam68 results in reduced survival of lung cancer patients. Based on these observations, an electrochemical immunosensor was developed for the quantification of Sam68 protein. The target protein was captured by the Anti-Sam68 antibody that was immobilized on the modified Glassy carbon electrode. The stepwise assembly process was characterized by cyclic voltammetry and electrochemical impedance spectroscopy. This fabricated immunosensor displayed good analytical performance in comparison to commercial ELISA kit with good sensitivity, lower detection limit (LOD) of 10.5 pg mL-1, and wide linear detection range from 1 to 5 µg mL-1. This method was validated with satisfactory detection of Sam68 protein in lung adenocarcinoma cell line, NCI-H23. Besides, spike and recovery assay reconfirm that the sensor can precisely quantify Sam68 protein in a complex physiological sample. CONCLUSION: We conclude Sam68 as a valuable prognostic biomarker for early detection of lung cancer. Moreover, we report the first study on the development of an electrochemical immunosensor for the detection of Sam68. The fabricated immunosensor exhibit excellent analytical performance, which can accurately predict the lung cancer patient pathological state.

Small-world networks of prognostic genes associated with lung adenocarcinoma development.

The present study investigates the role of network topology in lung adenocarcinoma (LUAD) development. Analysis of sex- and stage-specific whole-genome expression data revealed that co-expressed and highly connected prognostic genes common to all cancer stages form a small-world network in each stage of LUAD. These small-world networks are present within stage-specific scale-free networks, conserved across the cancer stages, and linked to cancer-specific events. The presence of small-world networks across the cancer stages presents a synchronized system in the disordered environment of cancer, resulting in the evolution of malignancy. Our study reported that these small-world networks are resilient to random and systematic attacks, indicating the least opportunity to introduce perturbations by drugs as a therapeutic intervention. We concluded that highly clustered small-world networks could be controlled through transcriptional modulation for the improved treatment of LUAD.