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In addressing the pressing environmental challenges posed by frequent oil spills, this work presents a novel approach of synthesizing a superhydrophobic three-dimensional (3D) porous melamine sponge (MS). CuO and ZnO nanoparticles were grown on the MS via a hydrothermal method to create MS/CuO/ZnO with multiscale hierarchical nanostructures. The resulting material exhibited a stable water contact angle of 155° through various tests. MS/CuO/ZnO demonstrated exceptional oil absorption capacities (40-145 g/g and 0.83-0.99 mL.cm -3 ), surpassing 98% efficiency in oil separation, and retained reusability for 10 cycles. Impressively, the sponge achieved successful separation of oil/water emulsions with a permeation flux of 14870 L m -2 h -1 . The composite sponge, distinguished by its high photodegradation ability, can degrade both water- and oil-targeted pollutants under visible light irradiation from light-emitting diode (LED). With its remarkable attributes including superior oil absorption, excellent oil/water separation, mechanical resistance, and excellent photocatalytic ability, it exhibits considerable potential for applications in both wastewater treatment and large-scale marine oil spill response. The easily prepared MS/CuO/ZnO emerges as a versatile solution capable of addressing pressing challenges and marking a significant leap toward sustainable and impactful environmental remediation.

Oriental theileriosis caused by Theileria orientalis, previously considered a benign disease, is posing a significant threat to the livestock industry across the globe. To elucidate the prevalence of Theileria orientalis in ticks and their host, the Mithun, a comprehensive study was undertaken in the two northeastern states of India, viz. Nagaland and Arunachal Pradesh. A total of 340 of Rhipicephalus microplus ticks and 25 Ambylomma sp. ticks were screened for the presence of Theileria orientalis through PCR. Among the R. microplus ticks examined, 25 of them tested positive for T. orientalis infection whereas none of the Amblyomma ticks was positive. Additionally, a total of 275 blood samples were collected from Mithun from Arunachal and Nagaland and 31 animals were found to be positive for T. orientalis infection. Notably, six positive cases were identified in Porba (Phek district), six in Tening, and one in Bamsiakilwa village (Peren district) of Nagaland. Moreover, out of the 41 animals examined at Medziphema farms, Nagaland, 18 were found to be positive for T. orientalis infection. Moreover, the phylogenetic investigation has unveiled the presence of the highly pathogenic Type 2 (Ikeda) T. orientalis genotype in Mithun, supported by a strong bootstrap value of 100%. This study marks the initial documentation of oriental theileriosis in mithun. It underscores the need for vigilant monitoring and active surveillance of mithun populations in the northeastern states of India. Timely treatment of infected animals is imperative to avert economic losses for the farmers., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: To gain better insight into the extent of secondary bacterial and fungal infections in hospitalized patients in India, and to assess how these alter the course of coronavirus disease 2019 (COVID-19) so that control measures can be suggested., Methods: In this retrospective, multicentre study, the data of all patients who tested positive for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) on reverse transcriptase polymerase chain reaction (RT-PCR), admitted to hospital between March 2020 and July 2021, were accessed from the electronic health records of a network of 10 hospitals across five states in North India., Results: Of 19,852 patients testing positive for SARS-CoV-2 on RT-PCR and admitted to the study hospitals during the study period, 1940 (9.8%) patients developed secondary infections (SIs). Patients with SIs were, on average, 8 years older than patients without SIs (median age 62.6 vs 54.3 years; P <0.001). The risk of SIs was significantly ( P <0.001) associated with age, severity of disease at admission, diabetes, admission to the intensive care unit (ICU), and ventilator use. The most common site of infection was urine (41.7%), followed by blood (30.8%) and sputum/bronchoalveolar lavage/endotracheal fluid (24.8%); the least common was pus/wound discharge (2.6%). Gram-negative bacilli (GNB) were the most common organisms (63.2%), followed by Gram-positive cocci (GPC) (19.6%) and fungi (17.3%). Most patients with SIs were on multiple antimicrobials. The most commonly used antibiotics against GNB were beta-lactam/beta-lactamase inhibitors (76.9%), carbapenems (57.7%), cephalosporins (53.9%), and antibiotics against carbapenem-resistant Enterobacteriaceae (47.1%). Empirical use of antibiotics against GPC was seen in 58.9% of patients with SIs, and empirical use of antifungals was observed in 56.9% of patients with SIs. The average length of hospital stay for patients with SIs was almost twice as long as that of patients without SIs (median 13 vs 7 days). Overall mortality among patients with SIs (40.3%) was more than eight times higher than that among patients without SIs (4.6%). Only 1.2% of patients with SIs with mild COVID-19 at admission died, compared with 17.5% of those with moderate COVID-19 at admission and 58.5% of those with severe COVID-19 at admission ( P <0.001). The mortality rate was highest in patients with bloodstream infections (49.8%), followed by those with hospital-acquired pneumonia (47.9%), urinary tract infections (29.4%), and skin and soft tissue infections (29.4%). The mortality rate in patients with diabetes with SIs was 45.2%, compared with 34.3% in those without diabetes ( P <0.001)., Conclusions: SIs complicate the course of patients hospitalized with COVID-19. These patients tend to have a much longer hospital stay, a higher requirement for oxygen and ICU care, and a significantly higher mortality rate compared with those without SIs. The groups most vulnerable to SIs are patients with more severe COVID-19, elderly patients and patients with diabetes. Judicious empirical use of combination antimicrobials in these groups of vulnerable patients can save lives. It is desirable to have region- or country-specific guidelines for appropriate use of antibiotics and antifungals to prevent their overuse., (© 2022 The Author(s).)

The presence of ERG gene fusion; from developing prostatic intraepithelial neoplasia (PIN) lesions to hormone resistant high grade prostate cancer (PCa) dictates disease progression, altered androgen metabolism, proliferation and metastasis 1-3 . ERG driven transcriptional landscape may provide pro-tumorigenic cues in overcoming various strains like hypoxia, nutrient deprivation, inflammation and oxidative stress. However, insights on the androgen independent regulation and function of ERG during stress are limited. Here, we identify PGC1α as a coactivator of ERG fusion under various metabolic stress. Deacetylase SIRT1 is necessary for PGC1α-ERG interaction and function. We reveal that ERG drives the expression of antioxidant genes; SOD1 and TXN, benefitting PCa growth. We observe increased expression of these antioxidant genes in patients with high ERG expression correlates with poor survival. Inhibition of PGC1α-ERG axis driven transcriptional program results in apoptosis and reduction in PCa xenografts. Here we report a function of ERG under metabolic stress which warrants further studies as a therapeutic target for ERG fusion positive PCa., (© 2022. The Author(s).)

Motifs are the evolutionarily conserved patterns which are reported to serve the crucial structural and functional role. Identification of motif patterns in a set of protein sequences has been a prime concern for researchers in computational biology. The discovery of such a protein motif using existing algorithms is purely based on the parameters derived from sequence composition and length. However, the discovery of variable length motif remains a challenging task, as it is not possible to determine the length of a motif in advance. In current work, a k-mer based motif discovery approach called Pr[m], is proposed for the detection of the statistically significant un-gapped motif patterns, with or without wildcard characters. In order to analyze the performance of the proposed approach, a comparative study was performed with MEME and GLAM2, which are two widely used non-discriminative methods for motif discovery. A set of 7,500 test dataset were used to compare the performance of the proposed tool and the ones mentioned above. Pr[m] outperformed the existing methods in terms of predictive quality and performance. The proposed approach is hosted at https://bioserver.iiita.ac.in/Pr[m].

Olfaction transduction mechanism is triggered by the binding of odorants to the specific olfactory receptors (OR's) present in the nasal cavity. Different odorants stimulate different OR's due to the difference in shape, physical and chemical properties. In this paper, a deep neural network architecture DeepOlf, based on molecular features and fingerprints of odorants and ORs, to predict whether a chemical compound is a potential odorant or not along with its interacting OR is proposed. Odorant identification and Odorant-OR interaction were modeled as a binary classification through multiple classifiers. The evaluation of these classifier's performance showed that the deep-neural network framework not only fits data with better accuracy in comparison to other classical methods (SVM, RF, k-NN) but also able to predict odorant-OR interactions more accurately. To our knowledge, this study is the first realization of deep learning ideas for the problem of odorant and interacting OR prediction. The accuracy of DeepOlf was found to be 94.83 and 99.92 percent for the prediction of odorants and Odorant- OR interactions respectively. Comparison of DeepOlf prediction with the existing SVM based prediction server, ODORactor, showed that better performance can be achieved with the proposed deep learning approach. The DeepOlf tool can be accessed at https://bioserver.iiita.ac.in/deepolf/.