Solar-matched S-scheme ZnO/g-C3N4 for visible light-driven paracetamol degradation.

In pursuit of an efficient visible light driven photocatalyst for paracetamol degradation in wastewater, we have fabricated the ZnO/g-C3N4 S-Scheme photocatalysts and explored the optimal percentage to form a composite of graphitic carbon nitride (g-C3N4) with zinc oxide (ZnO) for enhanced performance. Our study aimed to address the urgent need for a catalyst capable of environmentally friendly degradation of paracetamol, a common pharmaceutical pollutant, using visible light conditions. Here, we tailored the band gap of a photocatalyst to match solar radiation as a transformative advancement in environmental catalysis. Notably, the optimized composite, containing 10 wt.% g-C3N4 with ZnO, demonstrated outstanding paracetamol degradation efficiency of 95% within a mere 60-min exposure to visible light. This marked enhancement represented a 2.24-fold increase in the reaction rate compared to lower wt. percentage composites (3 wt.% g-C3N4) and pristine g-C3N4. The exceptional photocatalytic activity of the optimized composite can be attributed to the band gap narrowing that closely matched the maximum solar radiation spectrum. This, coupled with efficient charge transfer mechanisms through S-scheme heterojunction formation and an abundance of active sites due to increased surface area and reduced particle size, contributed to the remarkable performance. Trapping experiments identified hydroxyl radicals as the primary reactive species responsible for paracetamol photoreduction. Furthermore, the synthesized ZnO/g-C3N4 composite exhibited exceptional photostability and reusability, underscoring its practical applicability. Thus, this research marks a significant stride towards the development of an effective and sustainable visible light photocatalyst for the removal of pharmaceutical contaminants from aquatic environments.

ISLAND: in-silico proteins binding affinity prediction using sequence information.

BACKGROUND: Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. METHOD: We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. RESULTS: We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software . CONCLUSION: This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.

Expression of leptospiral immunoglobulin-like protein by Leptospira interrogans and evaluation of its diagnostic potential in a kinetic ELISA.

The search for novel antigens suitable for improved vaccines and diagnostic reagents against leptospirosis led to the identification of LigA and LigB. LigA and LigB expression were not detectable at the translation level but were detectable at the transcription level in leptospires grown in vitro. Lig genes were present in pathogenic serovars of Leptospira, but not in non-pathogenic Leptospira biflexa. The conserved and variable regions of LigA and LigB (Con, VarA and VarB) were cloned, expressed and purified as GST-fusion proteins. Purified recombinant LigA and LigB were evaluated for their diagnostic potential in a kinetic ELISA (KELA) using sera from vaccinated and microscopic agglutination test (MAT)-positive dogs. Sera from vaccinated dogs showed reactivity to whole-cell antigens of leptospires but did not show reactivity in the KELA assay with recombinant antigens, suggesting a lack of antibodies to Lig proteins in the vaccinated animals. The diagnostic potential of recombinant Lig antigens in the KELA assay was evaluated by using 67 serum samples with MAT > or =1600, which showed reactivity of 76, 41 and 35% to rConA, rVarA and rVarB, respectively. These findings suggest that recombinant antigen to the conserved region of LigA and LigB can differentiate between vaccinated and naturally infected animals.

Predictive Factors of Suicide Attempt and Non-Suicidal Self-Harm in Emergency Department.

INTRODUCTION: Suicide is the third cause of mortality in America, second leading cause of death in developed countries, and one of the major health problems. Self-harm is self-inflicted damage to one's self with or without suicidal intent. In the present study, the predictive factors of suicide attempt and non-suicidal self-harm were evaluated in patients referred to emergency department (ED) with these problem. METHODS: The total number of 45 patients with suicide attempt or self-harm admitted to ED were included. Clinical symptoms, thoughts and behaviors of suicidal, and non-suicidal self-harm in these patients were evaluated at baseline. Suicidality, suicidal intent and ideation, non-suicidal self-injury, social withdrawal, disruptive behavior, and poor family functions were evaluated at admission time. Brief clinical visits were scheduled for the twelfth weeks. In the twelfth week, patients returned for their final visit to determine their maintenance treatment. Finally, data were analyzed using chi-squared and multiple logistic regression. RESULTS: Forty-five patients were included in the study (56.1% female). The mean age of patients was 23.3±10.2 years (range: 15-75; 33.3% married). Significant association of suicide and self-injury was presented at the baseline and in the month before attempting (p=0.001). The most important predictive factors of suicide and self-harm based on univariate analysis were depression (suicidal and non-suicidal items of Hamilton depression rating scale), anxiety, hopelessness, younger age, history of non-suicidal self-harm and female gender (p<0.05). The participants' quality of life analysis showed a significant higher quality in physical component summary (p=0.002), mental component summary (p=0.001), and general health (p=0.001) at follow up period. CONCLUSION: At the time of admission in ED, suicide attempt and non-suicidal self-harm are subsequent clinical markers for the patient attempting suicide again. The most independent predictive factors of suicide attempt and self-harm were poor family function, hopelessness, non-suicidality items of Hamilton depression rating scale, history of non-suicidal self-harm, and anxiety disorders.