Intergenerational trauma: A silent contributor to mental health deterioration in Afghanistan.

Multiple theories, including family systems, epigenetics, attachments, and many others, have proposed mechanisms for trauma transmission from generation to generation. Intergenerational trauma is today one of the most important psychosocial issues affecting Afghans' mental health and psychology, with the potential to affect subsequent generations. A variety of factors have impacted the mental health of the Afghan population over the years, including years of conflict, socioeconomic instability, natural disasters, chronic drought conditions, economic turmoil, and food insecurity, all of which have been exacerbated by recent political turbulence and the The Coronavirus pandemic COVID-19 pandemic that has further increased the susceptibility to intergenerational trauma among the Afghan population. International bodies must play a role in addressing intergenerational trauma among Afghans. Breaking the chain in future generations will be possible by resolving political issues, providing adequate health facilities, financial support, and eliminating stigmas associated with mental health issues.

The impact of COVID-19 followed by extreme flooding on vector borne diseases in Pakistan: A mini narrative review.

While the Coronavirus pandemic continues to spread havoc across the world, countries like Pakistan are faced with another challenge: the steady rise of vector borne diseases alongside a spike in COVID-19 cases. Moreover, signs and clinical manifestations of multiple arbovirus infections mimic those experienced in COVID-19, causing further complications in management and diagnosis. Without urgent adequate management and testing equipment, the recent surge of COVID-19 along with the steady rise in Vector Borne Diseases (VBDs) could collapse the exhausted Pakistani healthcare system. This article explores the impact of COVID-19 on the management, diagnosis, and treatment of the common arbovirus infections of Pakistan, including dengue (DENV), malaria, chikungunya (CHIKV), and other foreign infections that are on a hazardous rise.

A pilot study of vidofludimus calcium for treatment of primary sclerosing cholangitis.

The purpose of this pilot study was to explore the efficacy, safety, and tolerability of vidofludimus calcium (VC) in the treatment of primary sclerosing cholangitis (PSC). This was a single-arm open-label pilot study with a cohort of 18 patients with PSC. Study patients received VC for a period of 6 months. The study was undertaken at two sites, Mayo Clinic, Rochester, MN, and Mayo Clinic, Phoenix, AZ. The primary endpoint of the study was improvement of serum alkaline phosphatase (ALP) at the end of the study. Secondary endpoints included assessment of other liver biomarkers (bilirubin, alanine aminotransferase, and aspartate aminotransferase). Of 18 patients enrolled, 11 completed the 6 months of study treatment. Patients who completed treatment versus those who did not were similar other than a significantly higher direct bilirubin at baseline in the group that completed treatment (mean ± SD, 0.4 ± 0.3 versus 0.1 ± 0.1, p = 0.04). By intent to treat analysis, the primary outcome was met in 16.7% (3/18) of patients. By per-protocol analysis, including only patients who completed treatment, normalization of ALP occurred in 27.7% (3/11) at week 24 (95% confidence interval, 6.0% to 61.0%). VC was well tolerated with no drug-related serious adverse events. Conclusion: This proof of concept study provides support for further exploration of VC in patients with PSC.

Synthesis, characterization, and computational study of copper bipyridine complex [Cu (C18H24N2) (NO3)2] to explore its functional properties.

In the present study, copper (II) complex of 4, 4'-di-tert-butyl-2,2'-bipyridine [Cu (C18H24N2) (NO3)2], 1 is investigated through its synthesis and characterization using elemental analysis technique, infra-red spectroscopy, and single-crystal analysis. The compound 1 crystallizes in orthorhombic space group P212121. The copper atom in the mononuclear complex is hexa coordinated through two nitrogen and four oxygen atoms from bipyridine ligand and nitrate ligands. The thermal analysis depicts the stability of the entitled compound up to 170 °C, and the decomposition takes place in different steps between 170 and 1000 °C. Furthermore, quantum chemical techniques are used to study optoelectronic, nonlinear optical, and therapeutic bioactivity. The values of isotropic and anisotropic linear polarizabilities of compound 1 are calculated as 41.65 × 10-24 and 23.02 × 10-24 esu, respectively. Likewise, the static hyperpolarizability is calculated as 47.92 × 10-36 esu using M06 functional compared with para-nitroaniline (p-NA) and found several times larger than p-NA. Furthermore, the antiviral potential of compound 1 is studied using molecular docking technique where intermolecular interactions are checked between the entitled compound and two crucial proteins of SARS-CoV-2 (COVID-19). Our investigation indicated that compound 1 interacts more vigorously to spike protein than main protease (MPro) due to its better binding energy of -9.60 kcal/mol compared with -9.10 kcal/mol of MPro. Our current study anticipated that the above-entitled coordination complexes could be potential candidates for optoelectronic properties and their biological activity.

DC-GAN-based synthetic X-ray images augmentation for increasing the performance of EfficientNet for COVID-19 detection.

Currently, many deep learning models are being used to classify COVID-19 and normal cases from chest X-rays. However, the available data (X-rays) for COVID-19 is limited to train a robust deep-learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high-dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep-convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID-19). To validate whether the generated images are accurate, we used the k-mean clustering technique with three clusters (Normal, Pneumonia, and COVID-19). We only selected the X-ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X-rays, we used the Grad-CAM technique to visualize the underlying pattern, which leads the network to its final decision.