Multi-location evaluation of mungbean (Vigna radiata L.) in Indian climates: Ecophenological dynamics, yield relation, and characterization of locations.

Crop yield varies considerably within agroecology depending on the genetic potential of crop cultivars and various edaphic and climatic variables. Understanding site-specific changes in crop yield and genotype × environment interaction are crucial and needs exceptional consideration in strategic breeding programs. Further, genotypic response to diverse agro-ecologies offers identification of strategic locations for evaluating traits of interest to strengthen and accelerate the national variety release program. In this study, multi-location field trial data have been used to investigate the impact of environmental conditions on crop phenological dynamics and their influence on the yield of mungbean in different agroecological regions of the Indian subcontinent. The present attempt is also intended to identify the strategic location(s) favoring higher yield and distinctiveness within mungbean genotypes. In the field trial, a total of 34 different mungbean genotypes were grown in 39 locations covering the north hill zone (n = 4), northeastern plain zone (n = 6), northwestern plain zone (n = 7), central zone (n = 11) and south zone (n = 11). The results revealed that the effect of the environment was prominent on both the phenological dynamics and productivity of the mungbean. Noticeable variations (expressed as coefficient of variation) were observed for the parameters of days to 50% flowering (13%), days to maturity (12%), reproductive period (21%), grain yield (33%), and 1000-grain weight (14%) across the environments. The genotype, environment, and genotype × environment accounted for 3.0, 54.2, and 29.7% of the total variation in mungbean yield, respectively (p < 0.001), suggesting an oversized significance of site-specific responses of the genotypes. Results demonstrated that a lower ambient temperature extended both flowering time and the crop period. Linear mixed model results revealed that the changes in phenological events (days to 50 % flowering, days to maturity, and reproductive period) with response to contrasting environments had no direct influence on crop yields (p > 0.05) for all the genotypes except PM 14-11. Results revealed that the south zone environment initiated early flowering and an extended reproductive period, thus sustaining yield with good seed size. While in low rainfall areas viz., Sriganganagar, New Delhi, Durgapura, and Sagar, the yield was comparatively low irrespective of genotypes. Correlation results and PCA indicated that rainfall during the crop season and relative humidity significantly and positively influenced grain yield. Hence, the present study suggests that the yield potential of mungbean is independent of crop phenological dynamics; rather, climatic variables like rainfall and relative humidity have considerable influence on yield. Further, HA-GGE biplot analysis identified Sagar, New Delhi, Sriganganagar, Durgapura, Warangal, Srinagar, Kanpur, and Mohanpur as the ideal testing environments, which demonstrated high efficiency in the selection of new genotypes with wider adaptability.

A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients.

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

A Vital Sign-based Prediction Algorithm for Differentiating COVID-19 Versus Seasonal Influenza in Hospitalized Patients.

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3,883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3,125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs. COVID-19-positive model had an AUC of 98%, and 92% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may be have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

A Vital Sign-based Prediction Algorithm for Differentiating COVID-19 Versus Seasonal Influenza in Hospitalized Patients

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3,883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3,125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs. COVID-19-positive model had an AUC of 98%, and 92% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may be have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

Verapamil as an Adjunct to Local Anaesthetic for Brachial Plexus Blocks.

BACKGROUND: Calcium channel blockers potentiate the effects of local anaesthetics. We examined the effect of adding verapamil to local anaesthetic solution on anaesthetic duration in patients undergoing surgery under brachial plexus block. METHODS: This study was a prospective, randomized, controlled, double blind study. Sixty patients undergoing elective upper limb surgery were divided into two groups of 30 each. Group A received 40 ml of 1% lignocaine with 0.25% bupivacaine, while Group B patients had 2.5 mg verapamil added. RESULT: Onset of sensory blockade time was marginally faster in Group B (23.2 ± 3.94 minutes) as compared to Group A (23.9 ± 4.13 minutes). However this difference was statistically not significant. The increase in duration of sensory blockade in Group B (185 ± 46.52 minutes) as compared to Group A (157 ± 44.28 minutes) was statistically significant (p= 0.011). Increase in duration of motor blockade in Group B (161 ± 46.14 minutes) as compared to Group A (149 ± 42,76 minutes) was statistically not significant (p = 0.15). Similarly prolongation of analgesic duration in Group B (318 ± 69.54minutes) as compared to Group A (302 ± 0.69 minutes) was statistically not significant (p=0.18). CONCLUSION: We conclude that adding verapamil to brachial plexus block can prolong sensory anaesthesia without any effect on analgesic duration.

Comparative Study of Recovery after Sevoflurane versus Halothane Anaesthesia in Adult Patients.

BACKGROUND: Induction and maintenance characteristics of sevoflurane and halothane have been studied, but little work has been done to compare the postoperative recovery of these two agents. METHODS: Sixty adult, ASA I and II patients were allocated randomly into Group A and Group B of 30 each. Group A received sevoflurane and Group B received halothane for maintenance. At the end of surgery early recovery, intermediate recovery and discharge criteria were assessed. RESULTS: Early recovery assessed with the mean time to extubation was 6.7 ± 2.29 min in Group A and 9.07 ± 1.64 min in Group B; eye opening was 7.28 ± 2.3 min in Group A and 10.6 ± 1.77 min in Group B; response to verbal command was 8.52 ± 2.83 min in Group A and 12.33 ± 2.17 min in Group B, while orientation was 10.43 ± 3.15 min in Group A and 14.77 ± 2.66 min in Group B. These differences were statistically significant (p<0.001). The mean time to reach post anaesthesia care unit discharge criteria was shorter for Group A (21.1 ± 4.69 min) as compared to Group B (27.43 ± 6.51 min) and this difference was statistically significant (p<0.001). CONCLUSION: Early recovery time and time taken to achieve discharge criteria were faster with sevoflurane.

PAIN ABDOMEN AT EXTREME ALTITUDES: SINISTER IMPLICATIONS.

2 cases of portal, splenic and superior mesenteric vein thromboses related to prolonged stay at high altitude are presented. Both presented initially with innocuous appearing vague pain in abdomen with no physical signs. Later hepatosplenomegaly, and pleural effusion (left) was also detected. Diagnosis was based on CT scan and colour Doppler study showing thrombosed veins and porto-systemic collateral. 1 patient developed a large splenic haematoma requiring splenectomy. Both were managed with early anticoagulation and have done well in the short follow up.