Syed Ahmed Zaki

Scrub typhus is a mite-borne rickettsial infection caused by Orientia tsutsugamushi and is transmitted through the bite of larval trombiculid mites. It is an important cause of acute febrile illness in endemic regions. Although the disease typically presents with fever, rash, lymphadenopathy, and eschar, its clinical manifestations are highly variable and may include severe systemic complications. We report a case of scrub typhus presenting with symptomatic hypocalcaemia and hypophosphataemia probably the result of renal tubular electrolyte loss, highlighting a potentially under-recognized metabolic manifestation.

Scrub typhus, caused by Orientia tsutsugamushi , is a zoonotic infection endemic to the Asia-Pacific region. Its severity ranges from mild illness to life-threatening complications and case fatality rate upto 30%, highlighting the importance of early diagnosis. This study analyzed the clinical profile and pregnancy outcomes of 34 pregnant women diagnosed with scrub typhus at a tertiary care and referral centre. Poor foetal outcomes were observed in 18 (53%) pregnancies with intra-uterine deaths and spontaneous abortion occurring in 12 cases, while six had preterm births between 32 and 35 weeks of gestation. There was no maternal mortality. In endemic regions, scrub typhus should be considered in all pregnant women presenting with unexplained fever. Early diagnosis and prompt treatment are crucial to improving maternal and foetal outcomes.

Benign acute childhood myositis (BACM) is a rare, self-limiting inflammatory condition affecting the skeletal muscle, typically emerging during the early convalescent phase of viral infections. Most patients recover with bed rest, analgesics and adequate hydration. However, atypical presentations can sometimes lead to delayed diagnoses and unnecessary investigations. We report five cases of BACM with unusual presentations at admission: toe walking, crawling, delayed recovery, rhabdomyolysis and recurrence. All patients experienced favourable outcomes and complete recovery upon follow-up. This case series highlights the diverse presentations of BACM, emphasising the importance of increasing awareness for early diagnosis, reducing unnecessary workups and providing proper counselling for parents.

The emergence of the SARS-CoV-2 Omicron variant, classified as a Variant of Concern (VoC) in November 2021, marked a significant shift in the COVID-19 landscape. This study investigates the subsequent development of a novel Omicron sublineage, JN.1, which displays distinctive mutations in the spike protein. The study delves into the phylogenetic differences between these variants and their potential implications. A comprehensive analysis of the genomic profiles and mutation patterns of JN.1 and BA.2.86 was conducted, utilizing SARS-CoV-2 database. The study explores the unique mutations, such as S:L455S in JN.1, associated with increased transmissibility and immune escape. Furthermore, a comparison with prevalent strains like XBB.1.5 and HV.1 highlights the substantial genetic divergence of JN.1. JN.1, first detected in August 2023, exhibits a notable spike protein mutation profile, including the reappearance of earlier variants’ mutations (E484K and P681R). The variant’s increased transmissibility and immune evasion potential are attributed to specific spike protein mutations like R21T, S50L, V127F, R158G, and others. The study also explores the distribution and prevalence of JN.1 globally, with a focus on the rising cases in India. JN.1 poses a unique challenge as one of the most immune-evading variants, with potential implications for COVID-19 transmission. The study emphasizes the importance of monitoring and understanding emerging variants, especially those with distinct spike protein mutations. The observed cases in India highlight the need for vigilance and prompt public health responses. As JN.1 continues to evolve, ongoing surveillance, vaccination strategies, and adherence to preventive measures are crucial to mitigating its potential impact on global public health.

Acute diarrhoeal illness remains a common medical problem in children with nearly 1.7 billion cases globally every year. We report five infants who, following severe diarrhoea, developed methaemoglobinemia. This is an altered state of haemoglobin presenting with cyanosis and can pose a diagnostic dilemma. It should be suspected in young infants without cyanotic heart disease presenting with severe diarrhoea, sepsis and cyanosis disproportionate to their clinical status. Its outcome depends on prompt treatment, the severity of underlying sepsis and co-morbidity.

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)–VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis.