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Alzheimer’s disease (AD) is an incurable neurodegenerative disorder accounting for 70%–80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25 AD) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.

Quinoline and its derivatives are widely studied by both synthetic and computational chemists due to their exciting perspectives in biological and nonlinear optical (NLO) research. Herein, three novel arylated quinolines: 3-(4-acetylphenyl)quinoline (1), 3-(4-(methylthio)phenyl)-quinoline (2) and 3-(4-phenoxyphenyl)quinoline (3) were synthesized employing Pd catalyzed Suzuki–Miyaura cross-coupling reaction. The chemical structures of all compounds were resolved employing different analytical techniques like 1H-NMR, FT-IR, UV–Vis, EIMS, elemental analysis and finally confirmed by single crystal X-ray diffraction analysis. Synthesized compounds were further subjected to density functional theory (DFT) calculations at B3LYP level of theory in conjunction with 6-311 + G(2d,p) basis set to explore optimized geometry, natural bond orbital (NBO) analysis, FT-IR spectroscopic data, frontier molecular orbitals (FMOs) and NLO properties. Overall, a good agreement was found between DFT computed results and corresponding experimental findings. Vertical electronic transition states were computationally calculated using time-dependent DFT (TDDFT) at same B3LYP level of theory and 6-311 + G(2d,p) basis set combination. NBO calculations indicated the occurrence of intra-molecular charge transfer in synthesized compounds, hence enormous molecular stability owing to hyperconjugative interactions. Energy of FMOs was used to calculate the global reactivity descriptors which indicated that synthesized molecules are chemically hard compounds with greater kinetic stability and electron donating capability. NLO properties were found to be in the range 1830–2960 a.u and order of 2 > 1 > 3. Urea molecule comparative analysis and two-state model confirmed that synthesized molecules are excellent NLO candidates and may have prospective uses in the technology related applications.Graphical abstract

Herein, we report the quantum chemical results based on density functional theory for the polarizability (α) and first hyperpolarizability (β) values of diacetylene-functionalized organic molecules (DFOM) containing an electron acceptor (A) unit in the form of nitro group and electron donor (D) unit in the form of amino group. Six DFOM 1–6 have been designed by structural tailoring of the synthesized chromophore 4,4′-(buta-1,3-diyne-1,4-diyl) dianiline (R) and the influence of the D and A moieties on α and β was explored. Ground state geometries, HOMO-LUMO energies, and natural bond orbital (NBO) analysis of all DFOM (R and 1–6) were explored through B3LYP level of DFT and 6-31G(d,p) basis set. The polarizability (α), first hyperpolarizability (β) values were computed using B3LYP (gas phase), CAM-B3LYP (gas phase), CAM-B3LYP (solvent DMSO) methods and 6-31G(d,p) basis set combination. UV-Visible analysis was performed at CAM-B3LYP/6-31G(d,p) level of theory. Results illustrated that much reduced energy gap in the range of 2.212–2.809 eV was observed in designed DFOM 1–6 as compared to parent molecule R (4.405 eV). Designed DFOM (except for 2 and 4) were found red shifted compared to parent molecule R. An absorption at longer wavelength was observed for 6 with 371.46 nm. NBO analysis confirmed the involvement of extended conjugation and as well as charge transfer character towards the promising NLO response and red shift of molecules under study. Overall, compound 6 displayed large <α> and βtot, computed to be 333.40 (a.u.) (B3LYP gas), 302.38 (a.u.) (CAM-B3LYP gas), 380.46 (a.u.) (CAM-B3LYP solvent) and 24708.79 (a.u.), 11841.93 (a.u.), 25053.32 (a.u.) measured from B3LYP (gas), CAM-B3LYP (gas) and CAM-B3LYP (DMSO) methods respectively. This investigation provides a theoretical framework for conversion of centrosymmetric molecules into non-centrosymmetric architectures to discover NLO candidates for modern hi-tech applications.

Pathogenic bacteria present a major threat to human health, causing various infections and illnesses, and in some cases, even death. The accurate identification of these bacteria is crucial, but it can be challenging due to the similarities between different species and genera. This is where automated classification using convolutional neural network (CNN) models can help, as it can provide more accurate, authentic, and standardized results.In this study, we aimed to create a larger and balanced dataset by image patching and applied different variations of CNN models, including training from scratch, fine-tuning, and weight adjustment, and data augmentation through random rotation, reflection, and translation. The results showed that the best results were achieved through augmentation and fine-tuning of deep models. We also modified existing architectures, such as InceptionV3 and MobileNetV2, to better capture complex features. The robustness of the proposed ensemble model was evaluated using two data splits (7:2:1 and 6:2:2) to see how performance changed as the training data was increased from 10 to 20%. In both cases, the model exhibited exceptional performance. For the 7:2:1 split, the model achieved an accuracy of 99.91%, F-Score of 98.95%, precision of 98.98%, recall of 98.96%, and MCC of 98.92%. For the 6:2:2 split, the model yielded an accuracy of 99.94%, F-Score of 99.28%, precision of 99.31%, recall of 98.96%, and MCC of 99.26%. This demonstrates that automatic classification using the ensemble model can be a valuable tool for diagnostic staff and microbiologists in accurately identifying pathogenic bacteria, which in turn can help control epidemics and minimize their social and economic impact.

Organic materials development, especially in terms of nonlinear optical (NLO) performance, has become progressively more significant owing to their rising and promising applications in potential photonic devices. Organic moieties such as carbazole and quinoline play a vital role in charge transfer applications in optoelectronics. This study reports and characterizes the donor–acceptor–donor–π–acceptor (D–A–D–π–A) configured novel designed compounds, namely, Q3D1–Q3D3, Q4D1–Q1D2, and Q5D1. We further analyze the structure–property relationship between the quinoline–carbazole compounds for which density functional theory (DFT) and time-dependent DFT (TDDFT) calculations were performed at the B3LYP/6-311G(d,p) level to obtain the optimized geometries, natural bonding orbital (NBO), NLO analysis, electronic properties, and absorption spectra of all mentioned compounds. The computed values of λmax, 364, 360, and 361 nm for Q3, Q4, and Q5 show good agreement of their experimental values: 349, 347, and 323 nm, respectively. The designed compounds (Q3D1–Q5D1) exhibited a smaller energy gap with a maximum redshift than the reference molecules (Q3–Q5), which govern their promising NLO behavior. The NBO evaluation revealed that the extended hyperconjugation stabilizes these systems and caused a promising NLO response. The dipole polarizabilities and hyperpolarizability (β) values of Q3D1–Q3D3, Q4D1-Q1D2, and Q5D1 exceed those of the reference Q3, Q4, and Q5 molecules. These data suggest that the NLO active compounds, Q3D1–Q3D3, Q4D1–Q1D2, and Q5D1, may find their place in future hi-tech optical devices.

Cyclophilins (Cyps), a family of peptidyl-prolyl isomerases, play essential roles in the life cycle of coronaviruses by interacting with viral proteins and modulating host immune responses. In this systematic review, we examined cell culture, animal model, and clinical studies assessing the anti-viral efficacy of cyclosporine A (CsA, PubChem CID: 5284373) and its non-immunosuppressive derivatives against coronaviruses. CsA demonstrated robust anti-viral activity in vitro across a broad range of coronaviruses, including but not limited to HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2, with potent EC50 values in the low micromolar range. Non-immunosuppressive analogs such as Alisporivir and NIM811 exhibited similar inhibitory effects. In vivo, CsA treatment significantly reduced viral load, ameliorated lung pathology, and improved survival in coronavirus-infected animals. Clinical studies further indicated that CsA administration was associated with improved outcomes in COVID-19 patients, including reduced mortality and shorter hospital stays. Mechanistic studies revealed that CsA disrupts the formation of viral replication complexes, interferes with critical Cyp–viral protein interactions, and modulates innate immune signaling. These findings collectively demonstrate the therapeutic potential of cyclophilin inhibitors as broad-spectrum anti-virals against current and emerging coronaviruses.

Extreme heat waves are causing widespread concern for comprehensive studies on their ecological and societal implications. With the ongoing rise in global temperatures, precise forecasting of heatwaves becomes increasingly crucial for proactive planning and ensuring safety. This study investigates the efficacy of deep learning (DL) models, including Artificial Neural Network (ANN), Conolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), using five years of meteorological data from Pakistan Meteorological Department (PMD), by integrating Explainable AI (XAI) techniques to enhance the interpretability of models. Although Weather forecasting has advanced in predicting sunshine, rain, clouds, and general weather patterns, the study of extreme heat, particularly using advanced computer models, remains largely unexplored, overlooking this gap risks significant disruptions in daily life. Our study addresses this gap by collecting five years of weather dataset and developing a comprehensive framework integrating DL and XAI models for extreme heat prediction. Key variables such as temperature, pressure, humidity, wind, and precipitation are examined. Our findings demonstrate that the LSTM model outperforms others with a lead time of 1–3 days and minimal error metrics, achieving an accuracy of 96.2%. Through the utilization of SHAP and LIME XAI methods, we elucidate the significance of humidity and maximum temperature in accurately predicting extreme heat events. Overall, this study emphasizes how important it is to investigate intricate DL models that integrate XAI for the prediction of extreme heat. Making these models understood allows us to identify important parameters, improving heatwave forecasting accuracy and guiding risk-reduction strategies.

Coxiella burnetii  (Cb) is a hardy, stealth bacterial pathogen lethal for humans and animals. Its tremendous resistance to the environment, ease of propagation, and incredibly low infectious dosage make it an attractive organism for biowarfare. Current research on the classification of Coxiella and features influencing its presence in the soil is generally confined to statistical techniques. Machine learning other than traditional approaches can help us better predict epidemiological modeling for this soil-based pathogen of public significance. We developed a two-phase feature-ranking technique for the pathogen on a new soil feature dataset. The feature ranking applies methods such as ReliefF (RLF), OneR (ONR), and correlation (CR) for the first phase and a combination of techniques utilizing weighted scores to determine the final soil attribute ranks in the second phase. Different classification methods such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) have been utilized for the classification of soil attribute dataset for Coxiella positive and negative soils. The feature-ranking methods established that potassium, chromium, cadmium, nitrogen, organic matter, and soluble salts are the most significant attributes. At the same time, manganese, clay, phosphorous, copper, and lead are the least contributing soil features for the prevalence of the bacteria. However, potassium is the most influential feature, and manganese is the least significant soil feature. The attribute ranking using RLF generates the most promising results among the ranking methods by generating an accuracy of 80.85% for MLP, 79.79% for LR, and 79.8% for LDA. Overall, SVM and MLP are the best-performing classifiers, where SVM yields an accuracy of 82.98% and 81.91% for attribute ranking by CR and RLF; and MLP generates an accuracy of 76.60% for ONR. Thus, machine models can help us better understand the environment, assisting in the prevalence of bacteria and decreasing the chances of false classification. Subsequently, this can assist in controlling epidemics and alleviating the devastating effect on the socio-economics of society.

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

With the growing demand for the development of intranasal (IN) products, such as nasal vaccines, which has been especially highlighted during the COVID-19 pandemic, the lack of novel technologies to accurately test the safety and effectiveness of IN products in vitro so that they can be delivered promptly to the market is critically acknowledged. There have been attempts to manufacture anatomically relevant 3D replicas of the human nasal cavity for in vitro IN drug tests, and a couple of organ-on-chip (OoC) models, which mimic some key features of the nasal mucosa, have been proposed. However, these models are still in their infancy, and have not completely recapitulated the critical characteristics of the human nasal mucosa, including its biological interactions with other organs, to provide a reliable platform for preclinical IN drug tests. While the promising potential of OoCs for drug testing and development is being extensively investigated in recent research, the applicability of this technology for IN drug tests has barely been explored. This review aims to highlight the importance of using OoC models for in vitro IN drug tests and their potential applications in IN drug development by covering the background information on the wide usage of IN drugs and their common side effects where some classical examples of each area are pointed out. Specifically, this review focuses on the major challenges of developing advanced OoC technology and discusses the need to mimic the physiological and anatomical features of the nasal cavity and nasal mucosa, the performance of relevant drug safety assays, as well as the fabrication and operational aspects, with the ultimate goal to highlight the much-needed consensus, to converge the effort of the research community in this area of work.