Mini laparotomy for candy cane syndrome at the jejunojejunostomy after a second Roux Y Gastric bypass with multiple surgical history: a case report.

Background: Candy cane syndrome (CCS) is a rare and underreported complication, seldom occurring after bariatric surgeries, especially, the Roux-en-Y gastric bypass (RYGB) type. It refers to an excessively long-blind end of the alimentary limb, usually at the gastrojejunal (GJ) junction, and to a lesser extent, can occur at the jejunojejunal (JJ) junction, that may cause symptoms including abdominal pain, regurgitation, nausea, vomiting and reflux. However, its diagnosis can be challenging and misleading. Case Description: A 34-year-old woman with a multiple past surgical history presented with small bowel obstruction (SBO) symptoms following a second gastric bypass surgery. An esophagogastroduodenoscopy (EGD) was inconclusive, then a computed tomography (CT) scan was done, which reported intussusception. The patient underwent laparoscopy, which revealed an anastomosis with an extra 14 cm of single-loop bowel near the JJ junction rather than intussusception, leading to a diagnostic laparoscopy followed by a mini-laparotomy procedure. Adhesiolysis followed by a resection of the elongated blind end was done, hence, the diagnosis of CCS was established. The patient tolerated the surgery with a complete resolution of her symptoms; no subsequent complications were reported. Conclusions: The frequency of RYGB surgery and the number of past surgeries a patient might have undergone might correlate independently with the risk of developing CCS.

Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability.

Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model designed to optimize critical parameters, including filter number and size, stride padding, pooling techniques, activation functions, learning rate, batch size, and the number of layers. Our approach leverages two publicly available brain tumor MRI datasets for research purposes. The first dataset comprises a total of 7,023 human brain images, categorized into four classes: glioma, meningioma, no tumor, and pituitary. The second dataset contains 253 images classified as "yes" and "no." Our approach delivers exceptional results, demonstrating an average 94.25% precision, recall, and F1-score with 96% accuracy for dataset 1, while an average 87.5% precision, recall, and F1-score, with accuracy of 88% for dataset 2. To affirm the robustness of our findings, we perform a comprehensive comparison with existing techniques, revealing that our method consistently outperforms these approaches. By systematically fine-tuning these critical hyperparameters, our model not only enhances its performance but also bolsters its generalization capabilities. This optimized CNN model provides medical experts with a more precise and efficient tool for supporting their decision-making processes in brain tumor diagnosis.

Synthesis, characterization, and BSA binding studies of newfangled 2-phenylacetohydrazide derivatives.

Five 2-phenylacetohydrazide derivatives (BPAH = N'-benzylidene-2-phenylacetohydrazide, HBPAH = N'-(2-hydroxybenzylidene)-2-phenylacetohydrazide), PPAH = 2-phenyl-N'-3-phenylallylideneacetohydrazide, FMPAH = N'-(furan-2-ylmethylene)-2-phenylaceto hydrazide and EPAH = N'-ethylidene-2-phenylacetohydrazide were synthesized by the condensation of 2-phenylacetohydrazide with the corresponding aldehyde. The synthesized compounds were characterized by FTIR, 1D, and 2D NMR spectroscopy. The structure of the BPAH and PPAH were analyzed by single crystal X-ray diffraction analysis and in both crystallized compounds, the molecules adopted trans geometry around the -C[bond, double bond]N- (imine) functional group. To explore the pharmacological significance of these compounds, the binding ability of these compounds with Bovine Serum Albumin (BSA) was investigated using fluorescence spectroscopy. BPAH and PPAH showed the highest binding ability while EPAH, HBPAH, and FMPAH had lower binding ability to BSA molecules. Thermodynamic parameters ΔG, ΔH°, and ΔS° demonstrated that interactions of BSA with compounds BPAH, EPAH, FMAH, and HBPAH were exothermic while for PPAH it was endothermic. The negative enthalpy and entropy of the compounds BPAH, EPAH, FMAH, and HBPAH indicated that van der Waals' forces and hydrogen bonding played a major role in stabilizing the BSA binding with the molecules. Hydrophobic interactions were predominant in the binding of PPAH with BSA tends to interact with two sets of BSA binding sites with an increase in temperature.

Cupping Therapy as a Potential Complimentary Treatment for Meniere's Disease: A Case Report.

Meniere's disease is defined by the presence of three essential symptoms: episodic vertigo, tinnitus, and sensorineural hearing loss. The mainstay of its management constitutes lifestyle modification and medical and surgical therapies. Cupping therapy is an ancient treatment that is still widely used especially in the Middle East, Africa, and the United Kingdom. This study portraits the case of a 54-year-old patient suffering from long-standing Meniere's disease. The patient was treated with the routine treatment that was to no avail. It was decided that the patient undergoes cupping therapy. Over two years of monthly cupping therapy sessions, the patient reported a decrease in intensity and frequency of symptoms until its disappearance. Cupping therapy has shown a positive outcome on the patient. According to our search, there is a previous case report published in 2020 that shares multiple similarities with our case. Further studies on cupping therapy and its efficacy, mechanism of action, and complications on a larger scale are advised.

Dopant-Free Pyrene-Based Hole Transporting Material Enables Efficient and Stable Perovskite Solar Cells.

Dopant-free hole transporting materials (HTMs) is significant to the stability of perovskite solar cells (PSCs). Here, we developed a novel star-shape arylamine HTM, termed Py-DB, with a pyrene core and carbon-carbon double bonds as the bridge units. Compared to the reference HTM (termed Py-C), the extension of the planar conjugation backbone endows Py-DB with typical intermolecular π-π stacking interactions and excellent solubility, resulting in improved hole mobility and film morphology. In addition, the lower HOMO energy level of the Py-DB HTM provides efficient hole extraction with reduced energy loss at the perovskite/HTM interface. Consequently, an impressive power conversion efficiency (PCE) of 24.33 % was achieved for dopant-free Py-DB-based PSCs, which is the highest PCE for dopant-free small molecular HTMs in n-i-p configured PSCs. The dopant-free Py-DB-based device also exhibits improved long-term stability, retaining over 90 % of its initial efficiency after 1000 h exposure to 25 % humidity at 60 °C. These findings provide valuable insights and approaches for the further development of dopant-free HTMs for efficient and reliable PSCs.

Silver oxide doped iron oxide/alginate nanocomposite coated cotton cloth for selective catalytic reduction of potassium ferricyanide.

Silver oxide doped iron oxide (Ag2O-Fe2O3) nanocatalyst was prepared and coated on cotton cloth (CC) as well as wrapped in sodium alginate (Alg) hydrogel. Ag2O-Fe2O3 coated CC (Ag2O-Fe2O3/CC) and Ag2O-Fe2O3 wrapped Alg (Ag2O-Fe2O3/Alg) were utilized as catalysts in reduction reaction of 4-nitrophenol (4-NP), congo red (CR), methylene blue (MB) and potassium ferricyanide (K3[Fe(CN)6]). Ag2O-Fe2O3/CC and Ag2O-Fe2O3/Alg were found to be effective and selective catalyst for the reaction of K3[Fe(CN)6]. Further amount of catalyst, K3[Fe(CN)6] quantity, amount of NaBH4, stability of catalyst and recyclability were optimized for the reaction of K3[Fe(CN)6] reduction. Ag2O-Fe2O3/Alg and Ag2O-Fe2O3/CC were appeared to be the stable catalysts by maintaining high activity during recyclability tests showing highest reaction rate constants (kapp) of 0.3472 and 0.5629 min-1, correspondingly. However, Ag2O-Fe2O3/CC can be easily recovered as compared to Ag2O-Fe2O3/Alg by simply removing from the reaction which is the main advantage of Ag2O-Fe2O3/CC. Moreover, Ag2O-Fe2O3/Alg and Ag2O-Fe2O3/CC were also examined in real samples and found useful for K3[Fe(CN)6] reduction involving real samples. The Ag2O-Fe2O3/CC nanocatalyst is a cost and time saving material for economical reduction of K3[Fe(CN)6] and environmental safety.

Advancing brain tumor detection: harnessing the Swin Transformer's power for accurate classification and performance analysis.

The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four distinct categories: glioma, meningioma, non-tumor, and pituitary, leveraging a dataset comprising 2,870 images. Employing the Swin Transformer architecture, our method intricately integrates a multifaceted pipeline encompassing sophisticated preprocessing, intricate feature extraction mechanisms, and a highly nuanced classification framework. Utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model's behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. The standout performance parameter, accuracy, stands at an impressive 97%. This achievement outperforms established models like CNN, DCNN, ViT, and their variants in brain tumor classification. Our methodology's robustness and exceptional accuracy showcase its potential as a pioneering model in this domain, promising substantial advancements in accurate tumor identification and classification, thereby contributing significantly to the landscape of medical image analysis.

Non-enzymatic electrochemical detection of melamine in dairy products by using CuO decorated carbon nanotubes nanocomposites.

Melamine, a typical nitrogen enriched organic compound exhibiting great potential in the industrial sector, is exploited as an adulterant to inflate protein levels in dairy products, can pose serious threats to humans and therefore necessitates its swift detection and precise quantification at its first exposure. In this investigation, sensitive and reliable sensor probes were fabricated using CuO nanoparticles and its nanocomposites (NCs) with carbon nanotubes (CNTs), carbon black (CB), and graphene oxide (GO) to promptly quantify melamine in dairy products. The optical, morphological, and structural characteristics of the CuO-CNT NCs were achieved using diverse instrumental techniques including UV-visible spectroscopy, transmission electron microscopy, X- ray diffraction, Fourier-transform infrared spectroscopy, and X-ray photoelectron spectroscopy and etc. The fabrication of glassy carbon electrodes (GCE) was accomplished by coating CuO-CNT NCs through a binder (5 % nafion). These sensor probes demonstrated outstanding electrochemical sensor performance with CuO-CNT NCs/Nafion/GCE sensor probe in terms of very low limit of detection (0.27 nM), good linearity range (0.05-0.5 nM), and relatively high sensitivity (93.924 µA µM-1 m-2) for melamine under optimized experimental conditions. Furthermore, the performance of CuO-CNT NCs/Nafion/GCE coated sensor probes was practically validated for the selective melamine detection in the real sample analysis of commercially available milk brands, which revealed significant figures of merit in a very short response time of 10 s. From the results, it was concluded that the current study might be helpful in the development of an efficient commercial sensor based on ultra-sensitive transition metal oxides in the field of health care monitoring, food stuffs in a broader scale as well as food applications.

Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation.

BACKGROUND: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually. OBJECTIVE: This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation. METHODS: The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms. RESULTS: The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset. CONCLUSION: The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors.

Assessment of heavy metals accumulation by vegetables irrigated with different stages of textile wastewater for evaluation of food and health risk.

Wastewater irrigation for vegetable cultivation is greatly concerned about the presence of toxic metals in irrigated soil and vegetables which causes possible threats to human health. This study aimed to ascertain the accumulation of heavy metals (HMs) in edible parts of vegetables irrigated with different stages of textile dyeing wastewater (TDW). Bio-concentration factor (BCF), Estimated daily intake (EDI), and target hazard quotient (THQ) were computed to estimate human health risks and speculate the hazard index (HI) of adults and children with the consumption of HMs contaminated vegetables at recommended doses. Five vegetables (red amaranth, Indian spinach, cauliflower, tomato, and radish) in a pot experiment were irrigated with groundwater (T1) and seven stages of TDW (T2∼T8) following a randomized complete block design (RCBD) with three replications. Among the TDW stages, T8, T7, T4, and T5 exhibited elevated BCF, EDI, THQ, and HI due to a rising trend in the accumulation of Pb, Cd, Cr, and Ni heavy metals in the edible portion of the red amaranth, followed by radish, Indian spinach, cauliflower, and tomato. The general patterns of heavy metal (HM) accumulation, regarded as vital nutrients for plants, were detected in the following sequence: Zn > Mn/Cu > Fe. Conversely, toxic metals were found to be Cd/Cr > Ni > Pb, regardless of the type of vegetables. Principal Component Analysis (PCA) identified T8, T7, and T4 of TDW as the primary contributors to the accumulation of heavy metals in the vegetables examined. Furthermore, the analysis of the heavy metals revealed that the BCF, THQ, and HI values for all studied metals were below 1, except for Pb. This suggests that the present consumption rates of different leafy and non-leafy vegetables, whether consumed individually or together, provide a low risk in terms of heavy metal exposure. Nevertheless, the consumption of T8, T7, and T4 irrigated vegetables, specifically Indian spinach alone or in combination with red amaranth and radish, by both adults and children, at the recommended rate, was found to pose potential health risks. On the other hand, T2, T3, and T6 irrigated vegetables were deemed safe for consumption. These findings indicated that the practice of irrigating the vegetables with T8, T7, and T4 stages of TDW has resulted in a significant buildup of heavy metals in the soils and edible parts of vegetables which are posing health risks to adults and children. Hence, it is imperative to discharge the T8, T7, and T4 stages of TDW after ETP to prevent the contamination of vegetables and mitigate potential health risks.

Recent Advancements on Sustainable Electrochemical Water Splitting Hydrogen Energy Applications Based on Nanoscale Transition Metal Oxide (TMO) Substrates.

The development of green hydrogen generation technologies is increasingly crucial to meeting the growing energy demand for sustainable and environmentally acceptable resources. Many obstacles in the advancement of electrodes prevented water electrolysis, long thought to be an eco-friendly method of producing hydrogen gas with no carbon emissions, from coming to fruition. Because of their great electrical conductivity, maximum supporting capacity, ease of modification in valence states, durability in hard environments, and high redox characteristics, transition metal oxides (TMOs) have recently captured a lot of interest as potential cathodes and anodes. Electrochemical water splitting is the subject of this investigation, namely the role of transition metal oxides as both active and supportive sites. It has suggested various approaches for the logical development of electrode materials based on TMOs. These include adjusting the electronic state, altering the surface structure to control its resistance to air and water, improving the flow of energy and matter, and ensuring the stability of the electrocatalyst in challenging conditions. In this comprehensive review, it has been covered the latest findings in electrocatalysis of the Oxygen Evolution Reaction (OER) and Hydrogen Evaluation Reaction (HER), as well as some of the specific difficulties, opportunities, and current research prospects in this field.

Inclined Curved Crack Composite Beam: Experimental and Computational Analysis with Recycled Aluminum Composite Materials and Comparison with an Artificial Neural Network.

This research focuses on an inclined curved crack model for a recycled aluminum composite beam at various crack depths and locations. The inclined curved crack equation of the motion, by governing a free vibration curved beam with a different depth of crack, is solved computationally via the differential quadrature method (DQM) and experimentally; additionally, the result of the natural frequency has been compared with various depths of curvature. For the first four modes of cracked beams, the computational method's output is used to determine the natural frequencies associated with mode shapes. The outcomes of the computational results suggested a structural health monitoring system to detect deterioration in composite structures when modal parameters have changed. An experimental set of results was validated using MATLAB2019a, and the outcomes were compared with an artificial neural network.

Quality of Life in Kidney Donors: A Single-Center Experience.

BACKGROUND: Live kidney donation is used to treat end-stage renal disease, but it poses risks and decision-making challenges for donors. This study examines kidney donors' long-term quality of life (QOL). METHODS:  We conducted a cross-sectional survey on kidney donors between 1982 and 2018. We used the Short Form-36 (SF-36) (Arabic version) to measure the donors' QOL. Out of 60 donors contacted, 44 agreed to participate and responded. Demographic information, donor-recipient relationships, and specific questions about the donation were collected and analyzed. RESULTS: The mean age of the donors was 50.1 ± 11.7 years at follow-up, and 26 (59.1%) were males. Most donors were siblings and parents, accounting for 36.4% (n=16) each. The time since the donation was 111.5 ± 97.1 months. All donors decided voluntarily to donate and all of them would do it again if given a chance. However, one donor (2.3%) complained that the donation had caused problems in his marriage, while six donors (13.4%) experienced clinically relevant distress, and two donors (4.5%) experienced financial disadvantages. Donors had high QOL scores, with a mean score of 73.1 and 96.9 (on a scale of 1-100) for the eight subscales. The highest score was for role social functioning, while the lowest was for energy/fatigue. The mean scores for the four fatigue subscales were low, ranging from 61.8 to 86.8. The lowest score was for feeling calm and reassured, while the highest was for feeling frustrated. In social functioning, the highest score was for the effect of mental health on work performance (97.2). We analyzed various demographic factors and their correlation with QOL and found no significant correlation in most domains regarding sociodemographic characteristics. CONCLUSIONS: Our research indicates that most kidney donors have had a positive experience over the past four decades. Both male and female donors of all ages reported good long-term QOL, further enhanced by recognition and support from their families and friends. These findings provide further support for our current policy on organ donation.

Sensitive Cr3+ sensor based on novel poly(luminol-co-1,8-diaminonaphthalene)/CeO2/MWCNTs nanocomposites.

In this study, poly(luminol-co-1,8-diaminonaphthalene) (PLim-DAN) was synthesized and subsequently modified with MWCNTs and CeO2 NPs. The synthesized nanocomposites were analyzed using IR, SEM, TEM, and XRD. Furthermore, a comprehensive set of thermal behavior measurements were taken using TGA/DTG analysis. Next, the electroactivity of the developed nanocomposites was tested as an electrochemical sensor to measure the concentration of Cr3+ ions in phosphate buffers. The GCE adapted with the PLim-DAN/CeO2/CNTs-10% nanocomposite (NC) exhibited the highest current response among the other compositions and copolymers. The fabricated nanocomposite sensor showed high sensitivity, with a value of 19.78 µA µM-1 cm-2, and a low detection limit of 4.80 ± 0.24 pM. The analytical performance was evaluated by plotting a current calibration curve versus the concentration of Cr3+ ions. It was found to be linear (R2 = 0.9908) over the range of 0.1 nM to 0.1 mM, identified as the linear dynamic range (LDR). This electrochemical sensor demonstrated that it could be a useful tool for environmental monitoring by accurately detecting and measuring carcinogenic Cr3+ ions in real-world samples.

Fabrication and Investigation of Deformable Rubber-Carbon Nanotube-Glue Gel-Based Impedimetric and Capacitive Tactile Sensors for Pressure and Displacement Measurements.

Carbon nanotube-glue composite gel-based surface-type elastic sensors with a cylindrical shape deformable (flexible) metallic body were fabricated for tactile pressure and compressive displacement sensing. The fabrication of the sensors was performed using the rubbing-in technique. The effect of the pressure and the compressive displacement on the capacitance and the impedance of the sensors were investigated at various frequencies (in the range of 1 kHz to 200 kHz). It was found that under the effect of pressure from 0 to 9 g/cm2, the capacitance increased by 1.86 and 1.78 times, while the impedance decreased by 1.84 and 1.71 times at the frequencies of 1 kHz to 200 kHz, respectively. The effect of displacement on the impedance and the capacitance of the device was also investigated at various frequencies from 1 kHz to 200 kHz. The results showed that under the effect of compressive displacement up to 25 µm, the impedance of the sensors decreased on average by 1.19 times, while the capacitance increased by 1.09 times, accordingly. The frequency response of the displacement sensor showed that it matched with the low-pass filter. The obtained results are explained based on changes in the shape and geometrical parameters of the cylindrical-shaped conductive body. These results have also been explained on the basis of the distance between the conductive plates of the capacitive sensors during compression, which takes place under the effect of applied pressure or displacement. Moreover, the design of the sensors is simple and easy to fabricate, and their use is also earthy. The fabricated sensors have great potential for commercialization.

An efficient wastewater treatment through reduction of organic dyes using Ag nanoparticles supported on cellulose gum beads.

This work reports silver nanoparticles (AgNPs) supported on biopolymer carboxymethyl cellulose beads (Ag-CMC) serves as an efficient catalyst in the reduction process of p-nitrophenol (p-NP) and methyl orange (MO). For Ag-CMC synthesis, first CMC beads were prepared by crosslinking the CMC solution in aluminium nitrate solution and then the CMC beads were introduced into AgNO3 solution to adsorb Ag ions. Field emission scanning electron microscopy (FE-SEM) analysis suggests the uniform distribution of Ag nanoparticles on the CMC beads. The X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) analysis revealed the metallic and fcc planes of AgNPs, respectively, in the Ag-CMC catalyst. The Ag-CMC catalyst exhibits remarkable reduction activity for the p-NP and MO dyes with the highest rate constant (kapp) of a chemical reaction is 0.519 and 0.697 min-1, respectively. Comparative reduction studies of Ag-CMC with CMC, Fe-CMC and Co-CMC disclosed that Ag-CMC containing AgNPs is an important factore in reducing the organic pollutants like p-NP and MO dyes. During the recyclability tests, the Ag-CMC also maintained high reduction activity, which suggests that CMC protects the AgNPs from leaching during dye reduction reactions.

Recent Advances, Challenges, and Future Perspectives of ZnO Nanostructure Materials Towards Energy Applications.

In this approach, zinc oxide (ZnO) is a multipurpose substance with remarkable characteristics such as high sensitivity, a large specific area, non-toxicity, excellent compatibility, and a high isoelectric point, which make it attractive for discussion with some limitations. It is the most favorable possible option for the collection of nanostructures in terms of structure and their characteristics. The development of numerous ZnO nanostructure-based electrochemical sensors and biosensors used in health diagnosis, pharmaceutical evaluation, food hygiene, and contamination of the environment monitoring is described, as well as the production of ZnO nanostructures. Nanostructured ZnO has good chemical and temperature durability as an n-type semiconducting material, making it useful in a wide range of uses, from luminous materials to supercapacitors, batteries, solar cells, photocatalysis, biosensors, medicinal devices, and more. When compared to the bulk materials, the nanosized materials have both a higher rate of disintegration and a higher solubility. Furthermore, ZnO nanoparticles are regarded as top contenders for electrochemical sensors due to their strong electrochemical behaviors and electron transmission characteristics. The impact of many factors, including selectivity, sensitivity, detection limit, strength, and structures, arrangements, and their respective functioning processes, has been investigated. This study concentrated a substantial amount of its attention on the recent advancements that have been made in ZnO-based nanoparticles, composites, and modified materials for use in the application areas of energy storage and conversion devices as well as biological applications. Supercapacitors, Li-ion batteries, dye-sensitized solar cells, photocatalysis, biosensors, medicinal, and biological systems have been studied. ZnO-based materials are constantly analyzed for their advantages in energy and life science applications.

Progress and Perspectives on Promising Covalent-Organic Frameworks (COFs) Materials for Energy Storage Capacity.

In recent years, a new class of highly crystalline advanced permeable materials covalent-organic frameworks (COFs) have garnered a great deal of attention thanks to their remarkable properties, such as their large surface area, highly ordered pores and channels, and controllable crystalline structures. The lower physical stability and electrical conductivity, however, prevent them from being widely used in applications like photocatalytic activities and innovative energy storage and conversion devices. For this reason, many studies have focused on finding ways to improve upon these interesting materials while also minimizing their drawbacks. This review article begins with a brief introduction to the history and major milestones of COFs development before moving on to a comprehensive exploration of the various synthesis methods and recent successes and signposts of their potential applications in carbon dioxide (CO2 ) sequestration, supercapacitors (SCs), lithium-ion batteries (LIBs), and hydrogen production (H2 -energy). In conclusion, the difficulties and potential of future developing with highly efficient COFs ideas for photocatalytic as well as electrochemical energy storage applications are highlighted.

Next-Gen brain tumor classification: pioneering with deep learning and fine-tuned conditional generative adversarial networks.

Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2.

Synthesis, structure elucidation, SC-XRD/DFT, molecular modelling simulations and DNA binding studies of 3,5-diphenyl-4,5-dihydro-1H-pyrazole chalcones.

Deoxyribonucleic acid (DNA) acts as the most important intracellular target for various drugs. Exploring the DNA binding interactions of small bioactive molecules offers a structural guideline for designing new drugs with higher clinical efficacy and enhanced selectivity. This study presents the facile synthesis of pyrazoline-derived compounds (4a)-(4f) by reacting substituted chalcones with hydrazine hydrate using formic acid. The structure elucidation of substituted pyrazoline compounds was carried out using 1H-NMR, FT-IR and elemental analyses. While the crystal structures of two compounds (4a) and (4b) have been resolved by single-crystal X-ray diffraction (SC-XRD) analysis. Hirshfeld surface analysis also endorsed their greater molecular stability. Computational calculations at DFT/B3LYP/6-311++G(d,p) were executed to compare the structural properties (bond angle and bond length) and explore reactivity descriptors, frontier molecular orbitals (FMO), Mulliken atomic charges (MAC), molecular electrostatic potential (MEP) and electronic properties. All the compounds were evaluated for DNA binding interactions by UV-Vis spectrophotometric analysis. The results revealed that compounds (4a)-(4f) bind to DNA via non-covalent binding mode having binding constant values ranging from 1.22 × 103 to 6.81 × 104 M-1. The negative values of Gibbs free energy also proved the interaction of studied compounds with DNA as a spontaneous process. The findings of molecular docking simulations depicted that these studied compounds showed significant binding interactions with DNA and these results were consistent with experimental findings. Compound (4b) was concluded as the most potent compound of the series with the highest binding constant (4.95 × 104) and strongest binding affinity (-8.48 kcal/mol).Communicated by Ramaswamy H. Sarma.