Effects of the COVID-19 pandemic on semen quality in male partners of infertile couples: a hospital-based observational study.

The effects of the coronavirus disease 2019 (COVID-19) pandemic on male fertility have received considerable attention because human testes contain high levels of angiotensin-converting enzyme-2 receptors, through which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can enter. Early studies showed decreases in semen quality during and after recovery from COVID-19. However, no semen quality studies have examined the effects of widespread subclinical and mild disease, as well as changes in lifestyle, psychosocial behavior, intake of dietary supplements, and stress. This cross-sectional study compared semen quality parameters in male partners of infertile couples between men who underwent semen analysis before the COVID-19 pandemic (prepandemic group) and men who underwent semen analysis during the pandemic period (pandemic group); the analysis sought to clarify the overall effects of the pandemic. No participants in the pandemic group had experienced clinically overt disease. Among the 239 participants, mean body weight (P = 0.001), mean body mass index (P < 0.001), median sperm concentration (P = 0.014), total sperm count (P = 0.006), and total percentages of motile (P = 0.013) and abnormal cells (P < 0.001) were significantly greater in the pandemic group (n = 137) than those in the prepandemic group (n = 102). Among abnormal cells, the percentages of cells with excess residual cytoplasm (P < 0.001), head defects (P < 0.001), and tail defects (P = 0.015) were significantly greater in the pandemic group than those in the prepandemic group. With the exception of morphology, the overall semenogram results were better in the pandemic group than those in the prepandemic group.

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review.

Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges.

There is a need for some techniques to solve various problems in today's computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study's primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.

Homeopathic Medicines in Second Wave of COVID-19: Prognostic Factor Research.

BACKGROUND/OBJECTIVE: The clinical profile and course of COVID-19 evolved perilously in a second wave, leading to the use of various treatment modalities that included homeopathy. This prognostic factor research (PFR) study aimed to identify clinically useful homeopathic medicines in this second wave. METHODS: This was a retrospective, multi-centred observational study performed from March 2021 to May 2021 on confirmed COVID-19 cases who were either in home isolation or at COVID Care Centres in Delhi, India. The data were collected from integrated COVID Care Centres where homeopathic medicines were prescribed along with conventional treatment. Only those cases that met a set of selection criteria were considered for analysis. The likelihood ratio (LR) was calculated for the frequently occurring symptoms of the prescribed medicines. An LR of 1.3 or greater was considered meaningful. RESULTS: Out of 769 confirmed COVID-19 cases reported, 514 cases were selected for analysis, including 467 in home isolation. The most common complaints were cough, fever, myalgia, sore throat, loss of taste and/or smell, and anxiety. Most cases improved and there was no adverse reaction. Certain new symptoms, e.g., headache, dryness of mouth and conjunctivitis, were also seen. Thirty-nine medicines were prescribed, the most frequent being Bryonia alba followed by Arsenicum album, Pulsatilla nigricans, Belladonna, Gelsemium sempervirens, Hepar sulphuris, Phosphorus, Rhus toxicodendron and Mercurius solubilis. By calculating LR, the prescribing indications of these nine medicines were ascertained. CONCLUSION: Add-on use of homeopathic medicines has shown encouraging results in the second wave of COVID-19 in integrated care facilities. Further COVID-related research is required to be undertaken on the most commonly prescribed medicines.

Role of Dantabija, Haridra, and Zingiber (DHZ) combination to restore health and immunity in mild to moderate COVID-19 patients

Background: COVID-19 (SARS-CoV-2) has caused various clinical manifestations ranging from asymptomatic, minor flu-like symptoms to acute respiratory distress syndrome (ARDS), pneumonia, and even death. Early restriction of viruses is of utmost importance in controlling the spread of COVID-19. The present study aimed to evaluate the role of a common herbal extract combination of pomegranate (dantabija), turmeric (haridra), and zinger (DHZ) in mild to moderate covid cases. Methods: A hundred covid-positive subjects of mild to moderate severity have been randomized to control and study groups. The study population has been given the fixed-dose combination of DHZ as an adjuvant to standard treatment. Data have been analyzed using standard statistical tools. Finding: DHZ as an adjuvant helped in turning 83.33% of patients negative in the home quarantine group whereas 40% of patients in the hospitalized group turned negative with the addition of DHZ in the standard management. The percent negativity was lower in patients who received only standard management. Out of all patients, who did not receive DHZ, only 38% of patients in home quarantine and 32% in hospitalized patients became negative for COVID-19. Patients who received DHZ also showed improvement in blood pressure levels, oxygen levels as well as improvement in all symptoms associated with COVID-19 infections. Interpretation: DHZ has shown a promising effect in mild to moderate cases of COVID-19 as an adjuvant to the standard therapy. The study results indicated that the combination probably produces its effect by its immunomodulatory action.

Semen Quality in Males Suffering From COVID-19: A Pilot Study.

Background It is well known that some viral infections may affect male fertility. Coronavirus disease (COVID-19) can lead to multiorgan damage through the angiotensin-converting enzyme-2 receptor, abundant in testicular tissue. However, little information is available regarding the shedding of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in semen and its impact on spermatogenesis and fertility potential. We planned to investigate the presence of SARS-CoV-2 in the semen of COVID-19 males and to study the effect of COVID-19 on semen quality and sperm DNA fragmentation index. Material and method Thirty COVID-19 male patients aged 19-45 registered to AIIMS Patna hospital participated in the survey between October 2020 and April 2021. We conducted a real-time reverse transcriptase test on all the semen samples. Detailed semen analysis, including the sperm DNA Fragmentation Index, was done at first sampling that is during COVID-19. After 74 days of the first sampling, we obtained the second sampling and repeated all the above tests. Results All semen samples collected in the first and second sampling tested with real-time reverse transcription-polymerase chain reaction (RT-PCR) were negative for SARS-CoV-2. In the first sampling, semen volume, vitality, total motility, sperm concentration, total sperm count, % normal morphology, % cytoplasmic droplet, and fructose were significantly lower. In contrast, semen agglutination, % head defect, DNA Fragmentation Index, liquefaction time, semen viscosity, and leukocytes were increased. These findings were reversed at the second sampling but not to the optimum level. All these findings were statistically significant (p < 0.05 for all). Thus, COVID-19 negatively affects semen parameters, including sperm DNA fragmentation index. Conclusion Although we could not find SARS-CoV-2 in the semen, the semen quality remained poor until the second sampling. Assisted reproductive technology (ART) clinics and sperm banking facilities should consider assessing the semen of COVID-19 males and exclude men with a positive history of SARS-CoV-2 until their semen quality returns to normal.

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

There is a need for some techniques to solve various problems in today’s computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study’s primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.

Computational investigation of natural compounds as potential main protease (Mpro) inhibitors for SARS-CoV-2 virus.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is significantly impacting human lives, overburdening the healthcare system and weakening global economies. Plant-derived natural compounds are being largely tested for their efficacy against COVID-19 targets to combat SARS-CoV-2 infection. The SARS-CoV-2 Main protease (Mpro) is considered an appealing target because of its role in replication in host cells. We curated a set of 7809 natural compounds by combining the collections of five databases viz Dr Duke's Phytochemical and Ethnobotanical database, IMPPAT, PhytoHub, AromaDb and Zinc. We applied a rigorous computational approach to identify lead molecules from our curated compound set using docking, dynamic simulations, the free energy of binding and DFT calculations. Theaflavin and ginkgetin have emerged as better molecules with a similar inhibition profile in both SARS-CoV-2 and Omicron variants.

Role of cystatin C and calprotectin as potential early prognostic biomarkers in COVID-19 patients admitted to a dedicated COVID care facility.

Background: The COVID-19 pandemic has claimed millions of lives. A tool for early prediction of severity and mortality risk is desirable for better utilization of health care facilities. Several biomarkers like D-dimer, lactate dehydrogenase (LDH), C-reactive protein (CRP) and some recently explored biomarkers like serum cystatin C and serum calprotectin have been proposed as prognostic markers of COVID-19, but their role as prognostic markers is so far undefined. The present work attempted to investigate the possible role of serum cystatin C and serum calprotectin as prognostic tools to predict severity and outcome ahead of time. Material and Methods: This observational cohort study was carried out on 95 COVID-19 patients admitted to a dedicated COVID care facility from mid-October 2020 to January 2021. Serial estimations of serum cystatin C and serum calprotectin levels were done and assessed for significant difference between severe (NEWS 2 score ≥5) and non-severe (NEWS 2 score <5) groups, survivors and deceased and on the basis of comorbidities at each time points. Survival analysis was done based on the optimal thresholds for severity and mortality, calculated from the receiver operating characteristic (ROC). Result: The results showed that median cystatin C levels were significantly higher on the first day in the severe group (P < 0.001) and in patients with cardiovascular disease (P < 0.05), chronic lung disease (P = 0.009) and among patients who died (P < 0.05). It remained raised on day 3 in severe (P < 0.05) and deceased (P < 0.05) group. Serum calprotectin levels were significantly higher in patients with chronic lung disease (P = 0.008) and in those who died (P < 0.05). Conclusion: Serum cystatin C could be used as a tool for early prognosis and therapeutic decision-making for COVID-19 patients. Serum calprotectin seems to be a better marker of critical illness.

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.

Clinicodemographic profile and predictors of poor outcome in hospitalised COVID-19 patients: a single-centre, retrospective cohort study from India.

OBJECTIVES: Primary objective was to study the clinicodemographic profile of hospitalised COVID-19 patients at a tertiary-care centre in India. Secondary objective was to identify predictors of poor outcome. SETTING: Single centre tertiary-care level. DESIGN: Retrospective cohort study. PARTICIPANTS: Consecutively hospitalised adults patients with COVID-19. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome variable was in-hospital mortality. Covariables were known comorbidities, clinical features, vital signs at the time of admission and on days 3-5 of admission, and initial laboratory investigations. RESULTS: Intergroup differences were tested using χ2 or Fischer's exact tests, Student's t-test or Mann-Whitney U test. Predictors of mortality were evaluated using multivariate logistic regression model. Out of 4102 SARS-CoV-2 positive patients admitted during 1-year period, 3268 (79.66%) survived to discharge and 834 (20.33%) died in the hospital. Mortality rates increased with age. Death was more common among males (OR 1.51, 95% CI 1.25 to 1.81). Out of 261 cases analysed in detail, 55.1% were in mild, 32.5% in moderate and 12.2% in severe triage category. Most common clinical presentations in the subgroup were fever (73.2%), cough/coryza (65.5%) and breathlessness (54%). Hypertension (45.2%), diabetes mellitus (41.8%) and chronic kidney disease (CKD; 6.1%) were common comorbidities. Disease severity on admission (adjusted OR 12.53, 95% CI 4.92 to 31.91, p<0.01), coagulation defect (33.21, 3.85-302.1, p<0.01), CKD (5.67, 1.08-29.64, p=0.04), high urea (11.05, 3.9-31.02, p<0.01), high prothrombin time (3.91, 1.59-9.65, p<0.01) and elevated ferritin (1.02, 1.00-1.03, p=0.02) were associated with poor outcome on multivariate regression. A strong predictor of mortality was disease progression on days 3-5 of admission (adjusted OR 13.66 95% CI 3.47 to 53.68). CONCLUSION: COVID-19 related mortality in hospitalised adult patients at our center was similar to the developed countries. Progression in disease severity on days 3-5 of admission or days 6-13 of illness onset acts as 'turning point' for timely referral or treatment intensification for optimum use of resources.

Current Potential Therapeutic Approaches against SARS-CoV-2: A Review.

The ongoing SARS-CoV-2 pandemic is a serious threat to public health worldwide and, to date, no effective treatment is available. Thus, we herein review the pharmaceutical approaches to SARS-CoV-2 infection treatment. Numerous candidate medicines that can prevent SARS-CoV-2 infection and replication have been proposed. These medicines include inhibitors of serine protease TMPRSS2 and angiotensin converting enzyme 2 (ACE2). The S protein of SARS-CoV-2 binds to the receptor in host cells. ACE2 inhibitors block TMPRSS2 and S protein priming, thus preventing SARS-CoV-2 entry to host cells. Moreover, antiviral medicines (including the nucleotide analogue remdesivir, the HIV protease inhibitors lopinavir and ritonavir, and wide-spectrum antiviral antibiotics arbidol and favipiravir) have been shown to reduce the dissemination of SARS-CoV-2 as well as morbidity and mortality associated with COVID-19.

In silico prediction of potential inhibitors for the main protease of SARS-CoV-2 using molecular docking and dynamics simulation based drug-repurposing.

BACKGROUND: The rapidly enlarging COVID-19 pandemic caused by the novel SARS-corona virus-2 is a global public health emergency of an unprecedented level. Unfortunately no treatment therapy or vaccine is yet available to counter the SARS-CoV-2 infection, which substantiates the need to expand research efforts in this direction. The indispensable function of the main protease in virus replication makes this enzyme a promising target for inhibitors screening and drug discovery to treat novel coronavirus infection. The recently concluded α-ketoamide ligand-bound X-ray crystal structure of SARS-CoV-2 Mpro (PDB ID: 6Y2F) from Zhang et al. has revealed the potential inhibitor binding mechanism and the molecular determinants responsible for substrate binding. METHODS: For the study, we have targeted the SARS-CoV-2 Mpro for the screening of FDA approved antiviral drugs and carried out molecular docking based virtual screening. Further molecular dynamic simulation studies of the top three selected drugs carried out to investigated for their binding affinity and stability in the SARS-CoV-2 Mpro active site. The phylogenetic analysis was also performed to know the relatedness between the SARS-CoV-2 genomes isolated from different countries. RESULTS: The phylogenetic analysis of the SARS-CoV-2 genome reveals that the virus is closely related to the Bat-SL-CoV and does not exhibit any divergence at the genomic level. Molecular docking studies revealed that among the 77 drugs, screened top ten drugs shows good binding affinities, whereas the top three drugs: Lopinavir-Ritonavir, Tipranavir, and Raltegravir were undergone for molecular dynamics simulation studies for their conformational stability in the active site of the SARS-CoV-2 Mpro protein. CONCLUSIONS: In the present study among the library of FDA approved antiviral drugs, the top three inhibitors Lopinavir-Ritonavir, Tipranavir, and Raltegravir show the best molecular interaction with the main protease of SARS-CoV-2. However, the in-vitro efficacy of the drug molecules screened in this study further needs to be corroborated by carrying out a biochemical and structural investigation.