Introduction: Malaysia has one of the highest diabetes prevalence rates in Asia (1 in 5), with >50% fail to achieve optimal glycemic control. Continuous glucose monitoring (CGM) provides detailed insights beyond hemoglobin A1c (HbA1c), capturing daily glucose fluctuations and variability. Realworld data describing CGM patterns and their clinical associations in Malaysian adults with type 2 diabetes mellitus (T2DM) are limited. Methodology: This cross-sectional study analyzed CGM data from 25 Malaysian adults with T2DM, standardized over 14 days. CGM metrics included time in range (TIR), time above range (TAR), time below range (TBR), mean glucose, and coefficient of variation (CV). Weekday–weekend comparisons were performed, and correlations with age, diabetes duration, HbA1c, body mass index (BMI), treatment regimen, and complications were assessed. Results: Participants had a mean age of 56.2 ± 13.2 years (52% male), mean HbA1c 9.4 ± 2.7%, diabetes duration 10.7 ± 9.0 years, and BMI 29.8 ± 9.4 kg/m². Mean TIR, TAR, TBR, mean glucose, and CV were similar between weekdays and weekends (p >0.34 for all). TIR >70% was achieved by 52% of participants on weekdays and 48% on weekends, with no statistically significant difference (p = 0.75). Longer diabetes duration correlated with lower TIR (r = −0.62, p <0.001), higher mean glucose (r = 0.58, p = 0.002), and greater variability (r = 0.54, p = 0.004). Older age was associated with lower TIR (r = −0.48, p = 0.014) and higher mean glucose (r = 0.44, p = 0.025). Higher HbA1c correlated with lower TIR (r = −0.36, p = 0.04), higher TAR (r = 0.35, p = 0.05), greater variability (r = 0.51, p = 0.006), and increased target organ damage (ρ = 0.55, p = 0.004). BMI was not associated with TIR (r = −0.18, p = 0.38). Participants with ≥2 complications had lower TIR (55.8 ± 28.4% vs 76.7 ± 19.2%, p = 0.073), while insulin the Conclusion In Malaysian adults with T2DM, CGM metrics were similar between weekdays and weekends, indicating lifestyle differences had minimal impact on glycemic control. Longer diabetes duration, older age, higher HbA1c, multiple complications, and insulin therapy identified patients at highest risk for poor glycemic control, greater variability, and hypoglycemia. These findings support the use of CGM for risk stratification, individualized monitoring, and therapy optimization to reduce complications and hypoglycemia risk.
Adult ; Blood Glucose ; Blood Glucose Self-Monitoring ; Continuous Glucose Monitoring ; Diabetes Mellitus, Type 2

Introduction: ISPAD guidelines recommend continuous glucose monitoring (CGM) as the standard of care for paediatric type 1 diabetes (T1DM). However, a “real-world” adoption gap persists, particularly in resource-limited settings. The Introductions of the study were to evaluate CGM adoption prevalence, identify documented barriers, and compare glycemic outcomes between active and non-active users. Methodology: This retrospective review analyzed electronic medical records (EMR) of 125 paediatric T1DM patients at Hospital Putrajaya (2025). Data included CGM status, insulin delivery method, and documented barriers. Independent T-tests compared mean hemoglobin A1c (HbA1c) between groups, and multivariable logistic regression identified independent predictors of adoption. Results: Cohort mean age was 11.8 ± 3.8 years. Active CGM users were 32.8% (n = 41), of whom 29.3% (n = 12) utilized predominantly automated insulin delivery (AID) systems. The remaining 67.2% (n = 84) were classified as non-active users, comprising both never-users and ex-users (discontinued use). Active users achieved significantly lower mean HbA1c than non-active users (8.73% vs 9.86%; p <0.001), with no significant difference in rates of DKA (p = 0.564) or severe hypoglycemia (p = 0.250). Among never-users, 42.9% lacked documented technology counselling (p = 0.001). Multivariable analysis identified funding source as the sole independent predictor of CGM adoption (adjusted OR = 7.76, p <0.001). While the primary documented barrier was financial (31.0%), a lack of documented barriers was noted in 61.9% of non-active users. Conclusion A substantial technology gap exists, primarily driven by financial access rather than clinical demographics. The difference of 1.13% in HbA1c between groups underscores the need to address financial setbacks to improve technology access in Malaysia and prevent diabetes complications.
Child ; Blood Glucose ; Blood Glucose Self-Monitoring ; Continuous Glucose Monitoring ; Diabetes Mellitus, Type 1

Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique

Objective:The variability of the apnea-hypopnea index（AHI） measured in the first and second halves of the night is significant in patients with obstructive sleep apnea hypopnea syndrome（OSAHS）. This variation may be related to fluid redistribution caused by the supine position during sleep. Methods:Eighty-nine adult subjects were enrolled. Circumferences（neck, chest, waist, and calf） were measured before sleep onset and upon awakening. Polysomnography（PSG） was performed, and the night was divided into two halves based on the midpoint of total sleep time to calculate AHI for each half. The correlation between changes in AHI and changes in circumferences was analyzed. Results:Twenty simple snorers and sixty-nine OSAHS patients were included, with a median AHI of 22.6（11.8, 47.3） events/hour. Compared to pre-sleep measurements, there was no significant change in neck circumference upon awakening in the control group（P=0.073）, while reductions were observed in the other three measurements（P=0.006, P=0.038, P<0.001）. In the OSAHS group, neck circumference increased（P<0.001）, and reductions were noted in the other three measurements（P<0.001 for all）, with the most significant change observed in calf circumference 40.0（37.1, 42.0） cm to 38.0（35.8, 40.5） cm. Compared to the first half of the night, total AHI, supine AHI, and NREM AHI significantly decreased in the second half（P=0.010, P=0.031, P=0.001）, while no significant changes were observed in lateral AHI and REM AHI（P=0.988, P=0.530）. Further analysis revealed a significant relationship between increased chest circumference and decreases in NREM AHI, supine AHI, and supine NREM AHI（P=0.036, P=0.072, P=0.034）, as well as between decreased lateral position AHI and increased waist circumference（P=0.048）. Additionally, this study found a negative correlation between changes in calf circumference and changes in AHI（R=-0.24, P=0.048）, while neck circumference changes positively correlated with changes in AHI（R=0.26, P=0.03）. Conclusion:In OSAHS patients during the second half of sleep compared to before sleeping, chest circumference, waist circumference, and calf circumference decrease while neck circumference increases; total AHI, supine position AHI, and NREM period AHI decrease; increases in chest circumference are associated with decreases in NREM period AHI, supine position AHI, supine position NREM period AHI. There is nocturnal variability in AHI among OSAHS patients that may be associated with fluid shifts during sleep.
Humans ; Sleep Apnea, Obstructive/physiopathology* ; Male ; Female ; Polysomnography ; Fluid Shifts/physiology* ; Adult ; Middle Aged ; Neck ; Severity of Illness Index ; Sleep/physiology* ; Snoring/physiopathology*

Objective:To investigate the application value of intraoperative electrocochleography（ECochG） monitoring technique and insertion techniques in cochlear implant（CI） and analyze its relationship with postoperative residual hearing（RH） preservation. Methods:Thirty-one patients（35 ears） who received CI in our hospital from June 2022 to July 2024 were enrolled. The Advanced Bionics Active Insertion Monitoring（AIM） system was used for real-time ECochG monitoring during surgery. Intraoperative cochlear microphonics （CM） waveform changes were recorded and analyzed in relation to postoperative RH preservation. Results:①ECochG recordings were successfully obtained in 34 of 35 ears （97.1%）. ②According to Harris classification, there were 7 ears（20.6%） of Type A（rising）, 7 ears（20.6%） of Type C（declining）, 8 ears（23.5%） of Type CC（fluctuating）, and 12 ears（35.3%） of Type D（no response）. ③The total CM amplitude decrease was significantly moderately correlated with postoperative low-mid frequency hearing loss（r=0.67, P=0.017）. The total CM amplitude decrease was significantly moderately correlated with postoperative low frequency hearing loss（r=0.65, P=0.023）. ④For the mean amplitude variation, the Amax was 30.70 μV, the Amin was 8.64 μV, and the Aend was 18.27 μV. ⑤Sixteen cases completed postoperative follow-up, with an average low-mid frequency（125-1 000 Hz） residual hearing loss of 15.25 dB HL and a RH preservation rate of 87.5%. Conclusion:Intraoperative ECochG monitoring can effectively predict postoperative residual hearing changes, effectively guide surgical manipulation, and improve residual hearing preservation rate.
Humans ; Cochlear Implantation/methods* ; Audiometry, Evoked Response ; Cochlear Implants ; Male ; Female ; Adult ; Middle Aged ; Monitoring, Intraoperative ; Adolescent ; Young Adult ; Minimally Invasive Surgical Procedures ; Child ; Aged ; Postoperative Period

Objective:To investigate the relationship between the Palatopharyngeal Arch Staging System（PASS） and the severity of Obstructive Sleep Apnea（OSA）, as well as the patterns of airway collapse, while further assessing its clinical applicability. Methods:A total of 98 patients diagnosed with OSA at the Department of Otorhinolaryngology Head and Neck Surgery, Shenzhen University Affiliated Shenzhen Hospital, were recruited for this study. Data collected included basic demographic information, oropharyngeal laryngoscopy videos, results from awake laryngoscopy Muller tests, and indicators from sleep respiratory monitoring. The distribution of each PASS stage among patients with varying severities of OSA was compared. Additionally, both objective and subjective sleep indicators along with occurrences of airway collapse in OSA patients across different PASS stages were analyzed. Results:In total, 98 patients participated in this study. Statistically significant differences were observed in neck circumference, weight, Body Mass Index（BMI）, tongue position, and PASS stage when comparing mild-to-moderate OSA patients to those with severe OSA（P<0.05）. Furthermore, there were statistically significant variations in Apnea-Hypopnea Index（AHI）, minimum blood oxygen saturation levels, average blood oxygen saturation levels, oxygen desaturation index values, and total oxygen desaturation indices among OSA patients categorized by different PASS stages. Multiple comparisons revealed statistically significant differences in AHI as well as minimum and average blood oxygen saturation levels between patients at PASS 1 versus those at PASS 3（P<0.05）. Additionally, notable differences regarding oropharyngeal collapse rates among OSA patients across various PASS stages were identified; specifically between those at PASS stage 1 and those at PASS stage 3. Conclusion:The proportion of PASS stages for OSA varies across different severity levels. The severity of OSA and the degree of airway collapse in patients with varying PASS stages also exhibit significant differences. Patients classified as PASS 3 demonstrate a more severe form of OSA compared to those at PASS 1, with stage 3 being more susceptible to oropharyngeal collapse than its stage 1 counterpart. This assessment system is anticipated to address the current limitations in evaluating the lateral pharyngeal wall within the oropharynx.
Humans ; Sleep Apnea, Obstructive/pathology* ; Male ; Severity of Illness Index ; Female ; Middle Aged ; Polysomnography ; Adult ; Pharynx/physiopathology* ; Aged

Objective:To analyze the efficacy, influencing factors, and risk warning of multi-plane low-temperature plasma radiofrequency ablation（MLT-RFA） in the treatment of obstructive sleep apnea hypopnea syndrome（OSAHS）. Methods:A total of 118 OSAHS patients admitted from October 2022 to June 2024 were selected as the research subjects. They were divided into mild group（n=46）, moderate group（n=52）, and severe group（n=20） according to the severity of their condition. MLT-RFA treatment was used for all patients. After surgery, the results of polysomnography（PSG） and the changes in the Calier Sleep Apnea Quality of Life Index（SAQLI） were observed before and after treatment. The incidence of complications after treatment was recorded, and the clinical efficacy of the patients was evaluated. At the same time, they were divided into a treatment effective group（n=106） and an ineffective group（n=12） according to their effects. The general clinical data of the two groups were compared, and binary logistics regression analysis was conducted to identify independent factors that affect treatment efficacy and construct a model. ROC curve analysis was used to evaluate the diagnostic efficacy of the model. Results:The treatment effectiveness rate of the mild group was 93.48%, the moderate group was 90.38%, and the severe group was 80.00%. There was no statistically significant difference in the treatment effectiveness rate among the three groups（P>0.05）. The AHI of the mild group, moderate group, and severe group increased sequentially, while the LSaO2and SAQLI scores decreased sequentially. After treatment, the AHI of all three groups decreased compared to before treatment, while the LSaO2and SAQLI scores increased compared to before treatment, and the differences were statistically significant（P<0.05）. The pre-treatment AHI of the effective group was lower than that of the ineffective group, and the pre-treatment LSaO2and SAQLI were higher than those of the ineffective group, with statistically significant differences（P<0.05）. Pre-treatment LSaO2and pre-treatment SAQLI are independent factors affecting the efficacy of MLT-RFA（P<0.05）. The AUC of pre-treatment LSaO2, pre-treatment SAQLI, and combined prediction were 0.907, 0.763, and 0.947, respectively, with sensitivities of 0.896, 0.840, and 0.917, and specificities of 0.833, 0.667, and 0.887, respectively. Conclusion:MLT-RFA has a significant effect on the treatment of OSAHS, and the AHI, LSaO2, and SAQLI of patients before treatment can predict the treatment effect, with LSaO2 and SAQLI being independent influencing factors. The combinerd prediction model exhibits high diagnostic efficiency, sensitivity, and specificity.
Radiofrequency Ablation/methods* ; Plasma Gases ; Sleep Apnea, Obstructive/surgery* ; Polysomnography ; Postoperative Complications/epidemiology* ; Quality of Life ; Severity of Illness Index ; Treatment Outcome ; Humans

Sleep-related hypermotor epilepsy （SHE） is a rare type of epilepsy with a prevalence rate of approximately 1.8/100 000. This disease mainly manifests as complex motor behaviors during non-rapid eye movement sleep， such as leg kicking， arm waving， and sitting up. Since such symptoms are similar to non-epileptic disorders such as night terrors and sleepwalking and abnormal discharges may not be observed on electroencephalography， the diagnosis of SHE is quite challenging. Currently， there is still a lack of evidence from large-scale randomized controlled studies to support pharmacological treatment strategies for SHE， and related data in China remain scarce. This article reports a case of SHE， in order to provide a clinical reference for the diagnosis and medication treatment of this disease.
Polysomnography

Objective To investigate the sleep architecture of sleep related bruxism（SB）in adults and the sleep architecture of SB comorbid with obstructive sleep apnea hypopnea syndrome（OSAHS），as well as their correlation with age and other factors. Methods A total of 51 subjects with SB and 67 controls were included in this study to analyze the sleep architecture of SB and compare the sleep architecture of SB comorbid with different severities of OSAHS. Results Compared with the control group，the SB group had a younger age，increases in N1（%TST）and N2（%TST），a reduction in N3（%TST），and an increase in arousal index. The SB group was divided into non-OSAHS group（group 1），mild OSAHS group（group 2），and moderate-to-severe OSAHS group（group 3）. Group 1 had a younger age than group 2 and group 3，and group 3 had increases in body mass index（BMI），N1（%TST），oxygen desaturation index（ODI），and arousal index and a reduction in N3（%TST）. The Spearman's rank correlation analysis showed that BMI，N1（%TST），arousal index，and ODI increased with the increase in apnea-hypopnea index（AHI），while N3（%TST）decreased with the increase in AHI. The binary logistic regression analysis showed that SB was negatively correlated with age and was positively correlated with arousal index. Conclusion SB may affect sleep architecture by increasing light sleep，reducing deep sleep，and increasing the number of awakenings. There are changes in sleep architecture in case of SB comorbid with different severities of OSAHS. SB is negatively correlated with age and is positively correlated with arousal index.
Polysomnography

INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. We explored the use of ML with oximetry, demographic and anthropometric data to diagnose OSA. METHODS: A total of 2,996 patients were included for modelling and divided into test and training sets. Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. RESULTS: In the best performing four-class model (neural network model predicting no, mild, moderate or severe OSA), a prediction of moderate and/or severe disease had a combined PPV of 94%; one out of 335 patients had no OSA and 19 had mild OSA. In the best performing two-class model (logistic regression model predicting no-mild vs. moderate-severe OSA), the PPV for moderate-severe OSA was 92%; two out of 350 patients had no OSA and 26 had mild OSA. CONCLUSION Our study showed that the prediction of moderate-severe OSA in a tertiary setting with an ML approach is a viable option to facilitate early identification of OSA. Prospective studies with home-based oximeters and analysis of other oximetry variables are the next steps towards formal implementation.
Humans ; Oximetry/methods* ; Sleep Apnea, Obstructive/diagnosis* ; Male ; Female ; Middle Aged ; Machine Learning ; Polysomnography ; Adult ; Anthropometry ; ROC Curve ; Aged ; Algorithms ; Predictive Value of Tests ; Sensitivity and Specificity ; Neural Networks, Computer ; Demography