Anjan Kumar Saha

Malignant hyperthermia (MH) is a rare pharmacogenetic disorder triggered by volatile anesthetics and succinylcholine, most often linked to pathogenic variants in RYR1, CACNA1S, and STAC3. The advent of next-generation sequencing (NGS) has transformed MH diagnostics, offering new opportunities for perioperative risk assessment as caffeine–halothane contracture testing declines. However, challenges remain, including incomplete penetrance, variable pathogenicity of variants, limited access to functional confirmatory testing, and cost. Genetic testing also raises important questions. What is the clinical utility of finding a variant of unknown significance? What are the broader implications of MH susceptibility beyond the operating room? Emerging evidence connects MH susceptibility loci to exertional heat illness (EHI), exertional rhabdomyolysis (ERM), and heat-related mortality, highlighting the need for a broader framework for genetic risk assessment. This review synthesizes historical advances, current consensus, and future directions concerning MH to guide anesthesiologists and perioperative clinicians in leveraging molecular diagnostics for personalized care and improved patient safety.

Malignant hyperthermia (MH) is a rare pharmacogenetic disorder triggered by volatile anesthetics and succinylcholine, most often linked to pathogenic variants in RYR1, CACNA1S, and STAC3. The advent of next-generation sequencing (NGS) has transformed MH diagnostics, offering new opportunities for perioperative risk assessment as caffeine-halothane contracture testing declines. However, challenges remain, including incomplete penetrance, variable pathogenicity of variants, limited access to confirmatory testing, and cost. Genetic testing also raises important questions. What is the clinical utility of finding a variant of unknown significance? What are the broader implications of MH susceptibility beyond the operating room? Emerging evidence connects MH susceptibility loci to exertional heat illness and heat-related mortality, highlighting the need for a broader framework for genetic risk assessment. This review synthesizes historical advances, current consensus, and future directions concerning MH to guide anesthesiologists and perioperative clinicians in leveraging molecular diagnostics for personalized care and improved patient safety.

Centromere defects in Systemic Sclerosis (SSc) have remained unexplored despite the fact that many centromere proteins were discovered in patients with SSc. Here we report that lesion skin fibroblasts from SSc patients show marked alterations in centromeric DNA. SSc fibroblasts also show DNA damage, abnormal chromosome segregation, aneuploidy (only in diffuse cutaneous (dcSSc)) and micronuclei (in all types of SSc), some of which lose centromere identity while retaining centromere DNA sequences. Strikingly, we find cytoplasmic “leaking” of centromere proteins in limited cutaneous SSc (lcSSc) fibroblasts. Cytoplasmic centromere proteins co-localize with antigen presenting MHC Class II molecules, which correlate precisely with the presence of anti-centromere antibodies. CENPA expression and micronuclei formation correlate highly with activation of the cGAS-STING/IFN-β pathway as well as markers of reactive oxygen species (ROS) and fibrosis, ultimately suggesting a link between centromere alterations, chromosome instability, SSc autoimmunity, and fibrosis.

Background: During the initial months of the COVID-19 pandemic, rapidly rising disease prevalence in the United States created a demand for patient-facing information exchanges that addressed questions and concerns about the disease. One approach to managing increased patient volumes during a pandemic involves the implementation of telephone-based triage systems. During a pandemic, telephone triage hotlines can be employed in innovative ways to conserve medical resources and offer useful population-level data about disease symptomatology and risk factor profiles. Objective: The aim of this study is to describe and evaluate the COVID-19 telephone triage hotline used by a large academic medical center in the midwestern United States. Methods: Michigan Medicine established a telephone hotline to triage inbound patient calls related to COVID-19. For calls received between March 24, 2020, and May 5, 2020, we described total call volume, data reported by callers including COVID-19 risk factors and symptomatology, and distribution of callers to triage algorithm endpoints. We also described symptomatology reported by callers who were directed to the institutional patient portal (online medical visit questionnaire). Results: A total of 3929 calls (average 91 calls per day) were received by the call center during the study period. The maximum total number of daily calls peaked at 211 on March 24, 2020. Call volumes were the highest from 6 AM to 11 AM and during evening hours. Callers were most often directed to the online patient portal (1654/3929, 42%), nursing hotlines (1338/3929, 34%), or employee health services (709/3929, 18%). Cough (126/370 of callers, 34%), shortness of breath (101/370, 27%), upper respiratory infection (28/111, 25%), and fever (89/370, 24%) were the most commonly reported symptoms. Immunocompromised state (23/370, 6%) and age >65 years (18/370, 5%) were the most commonly reported risk factors. Conclusions: The triage algorithm successfully diverted low-risk patients to suitable algorithm endpoints, while directing high-risk patients onward for immediate assessment. Data collected from hotline calls also enhanced knowledge of symptoms and risk factors that typified community members, demonstrating that pandemic hotlines can aid in the clinical characterization of novel diseases.

BACKGROUND During the initial months of the COVID-19 pandemic, rapidly rising disease prevalence in the United States created a demand for patient-facing information exchanges that addressed questions and concerns about the disease. One approach to managing increased patient volumes during a pandemic involves the implementation of telephone-based triage systems. During a pandemic, telephone triage hotlines can be employed in innovative ways to conserve medical resources and offer useful population-level data about disease symptomatology and risk factor profiles. OBJECTIVE The aim of this study is to describe and evaluate the COVID-19 telephone triage hotline used by a large academic medical center in the midwestern United States. METHODS Michigan Medicine established a telephone hotline to triage inbound patient calls related to COVID-19. For calls received between March 24, 2020, and May 5, 2020, we described total call volume, data reported by callers including COVID-19 risk factors and symptomatology, and distribution of callers to triage algorithm endpoints. We also described symptomatology reported by callers who were directed to the institutional patient portal (online medical visit questionnaire). RESULTS A total of 3929 calls (average 91 calls per day) were received by the call center during the study period. The maximum total number of daily calls peaked at 211 on March 24, 2020. Call volumes were the highest from 6 AM to 11 AM and during evening hours. Callers were most often directed to the online patient portal (1654/3929, 42%), nursing hotlines (1338/3929, 34%), or employee health services (709/3929, 18%). Cough (126/370 of callers, 34%), shortness of breath (101/370, 27%), upper respiratory infection (28/111, 25%), and fever (89/370, 24%) were the most commonly reported symptoms. Immunocompromised state (23/370, 6%) and age >65 years (18/370, 5%) were the most commonly reported risk factors. CONCLUSIONS The triage algorithm successfully diverted low-risk patients to suitable algorithm endpoints, while directing high-risk patients onward for immediate assessment. Data collected from hotline calls also enhanced knowledge of symptoms and risk factors that typified community members, demonstrating that pandemic hotlines can aid in the clinical characterization of novel diseases.

Objective To develop a population-specific methodology for estimating glycaemic control that optimises resource allocation for patients with diabetes in rural Sri Lanka. Design Cross-sectional study. Setting Trincomalee, Sri Lanka. Participants Patients with non-insulin-treated type 2 diabetes (n=220) from three hospitals in Trincomalee, Sri Lanka. Outcome measure Cross-validation was used to build and validate linear regression models to identify predictors of haemoglobin A1c (HbA1c). Validation of models that regress HbA1c on known determinants of glycaemic control was thus the major outcome. These models were then used to devise an algorithm for categorising the patients based on estimated levels of glycaemic control. Results Time since last oral intake other than water and capillary blood glucose were the statistically significant predictors of HbA1c and thus included in the final models. In order to minimise type II error (misclassifying a high-risk individual as low-risk or moderate-risk), an algorithm for interpreting estimated glycaemic control was created. With this algorithm, 97.2% of the diabetic patients with HbA1c ≥9.0% were correctly identified. Conclusions Our calibrated algorithm represents a highly sensitive approach for detecting patients with high-risk diabetes while optimising the allocation of HbA1c testing. Implementation of these methods will optimise the usage of resources devoted to the management of diabetes in Trincomalee, Sri Lanka. Further external validation with diverse patient populations is required before applying our algorithm more widely.

Centromere genomics remain poorly characterized in cancer, due to technologic limitations in sequencing and bioinformatics methodologies that make high-resolution delineation of centromeric loci difficult to achieve. We here leverage a highly specific and targeted rapid PCR methodology to quantitatively assess the genomic landscape of centromeres in cancer cell lines and primary tissue. PCR-based profiling of centromeres revealed widespread heterogeneity of centromeric and pericentromeric sequences in cancer cells and tissues as compared to healthy counterparts. Quantitative reductions in centromeric core and pericentromeric markers (α-satellite units and HERV-K copies) were observed in neoplastic samples as compared to healthy counterparts. Subsequent phylogenetic analysis of a pericentromeric endogenous retrovirus amplified by PCR revealed possible gene conversion events occurring at numerous pericentromeric loci in the setting of malignancy. Our findings collectively represent a more comprehensive evaluation of centromere genetics in the setting of malignancy, providing valuable insight into the evolution and reshuffling of centromeric sequences in cancer development and progression.

Centromere genomics remain poorly characterized in cancer, due to technologic limitations in sequencing and bioinformatics methodologies that make high-resolution delineation of centromeric loci difficult to achieve. We here leverage a highly specific and targeted rapid PCR methodology to quantitatively assess the genomic landscape of centromeres in cancer cell lines and primary tissue. PCR-based profiling of centromeres revealed widespread heterogeneity of centromeric and pericentromeric sequences in cancer cells and tissue as compared to healthy counterparts. Quantitative reductions in select centromeric core and pericentromeric markers were observed in neoplastic samples as compared to healthy counterparts. Subsequent phylogenetic analysis of a pericentromeric endogenous retrovirus amplified by PCR revealed possible gene conversion events occurring at numerous pericentromeric loci in the setting of malignancy. Our findings collectively represent the first look into centromere genetics in the setting of malignancy, providing valuable insight into the evolution and reshuffling of centromeric sequences in cancer development and progression.