Potential damage to ovarian reserve from laparoscopic electrocoagulation in endometriomas and benign ovarian cysts: a systematic review and meta-analysis.

PURPOSE: Laparoscopic cystectomy for ovarian endometriomas and benign ovarian cysts is often conducted through hemostatic methods, with bipolar electrocoagulation as a common approach. This study evaluated the impact of electrocoagulation, primarily through bipolar energy, versus nonthermal hemostatic methods on ovarian reserve in patients undergoing laparoscopic cystectomy for ovarian endometriomas and benign ovarian cysts. METHODS: A systematic review with meta-analysis was conducted by searching the Cochrane Library, PubMed, EMBASE, and Web of Science databases. Randomized controlled trials (RCTs) comparing the impact of nonthermal hemostatic methods and electrocoagulation on the ovarian reserve during laparoscopic cystectomy were included. The Cochrane Risk of Bias Tool for Randomized Controlled Trials (ROB 2.0) was utilized to assess the quality of the included studies. The meta-analysis included 13 RCTs involving 1043 patients. Postoperative serum anti-Müllerian hormone (AMH) levels and antral follicle counts (AFCs) were analyzed using Review Manager ver. 5.4. RESULTS: Compared with the bipolar group, patients with endometriomas in the nonthermal hemostatic group exhibited significantly higher postoperative AMH levels at 1, 3, 6, and 12 months. Conversely, no significant differences in AMH levels were observed in patients with benign ovarian cysts. Similarly, AFCs showed no significant differences, except for lower postoperative AFCs in patients with endometrioma in the electrocoagulation group. CONCLUSION: Nonthermal hemostatic methods are associated with more effective preservation of the ovarian reserve compared with bipolar electrocoagulation in laparoscopic cystectomy for ovarian endometriomas. However, no significant impact of bipolar electrocoagulation on the ovarian reserve was observed in patients with benign ovarian cysts. TRIAL REGISTRATION: Registered in PROSPERO on April 10, 2023; ID # CRD42023413158.

The optimal measurement period of actigraphy for circadian rhythm in relation to adiposity: A retrospective case-control study.

BACKGROUND: This study focused on the relationship between adiposity and Rest-Activity Rhythms (RAR), utilizing both parametric cosine-based models and non-parametric algorithms. The emphasis was on the impact of varying measurement periods (7-28 days) on this relationship. METHODS: We retrieved actigraphy data from two datasets, encompassing a diverse cohort recruited from an obesity outpatient clinic and a workplace health promotion program. Participants were required to wear a research-grade wrist actigraphy device continuously for a minimum of four weeks. The final dataset included 115 individuals (mean age 40.7 ± 9.5 years, 51 % female). We employed both parametric and non-parametric methods to quantify RAR using six standard variables. Additionally, the study evaluated the correlations between three key adiposity indices - Body Mass Index (BMI), Visceral Adipose Tissue (VAT) area, and Body Fat Percentage (BF%) - and circadian rhythm indicators, controlling for factors like physical activity, age, and gender. RESULTS: The obesity group displayed a significantly lower relative amplitude (RA) as per non-parametric algorithm findings, with a decreased amplitude noted in the parametric algorithm analysis, in comparison to the overweight and control groups. The relationship between circadian rhythm indicators and adiposity metrics over 7- to 28-day periods was examined. A notable negative correlation was observed between RA and both BMI and VAT, while correlation coefficients between adiposity indicators and non-parametric circadian parameters increased with extended durations of actigraphy data. Specifically, RA over a 28-day period was significantly correlated with BF%, a trend not seen in the 7-day measurement (p = 0.094) in multivariate linear regression. The strength of the correlation between BF% and 28-day RA was more pronounced than that in the 7-day period (p = 0.044). However, replacing RA with amplitude as per parametric cosinor fitting yielded no significant correlations for any of the measurement periods. CONCLUSION: The study concludes that a 28-day measurement period more effectively captures the link between disrupted circadian rhythms and adiposity. Non-parametric algorithms, in particular, were more effective in characterizing disrupted circadian rhythms, especially when extending the measurement period beyond the standard 7 days.

From Sea to Science: Coral Aquaculture for Sustainable Anticancer Drug Development.

Marine natural products offer immense potential for drug development, but the limited supply of marine organisms poses a significant challenge. Establishing aquaculture presents a sustainable solution for this challenge by facilitating the mass production of active ingredients while reducing our reliance on wild populations and harm to local environments. To fully utilize aquaculture as a source of biologically active products, a cell-free system was established to target molecular components with protein-modulating activity, including topoisomerase II, HDAC, and tubulin polymerization, using extracts from aquaculture corals. Subsequent in vitro studies were performed, including MTT assays, flow cytometry, confocal microscopy, and Western blotting, along with in vivo xenograft models, to verify the efficacy of the active extracts and further elucidate their cytotoxic mechanisms. Regulatory proteins were clarified using NGS and gene modification techniques. Molecular docking and SwissADME assays were performed to evaluate the drug-likeness and pharmacokinetic and medicinal chemistry-related properties of the small molecules. The extract from Lobophytum crassum (LCE) demonstrated potent broad-spectrum activity, exhibiting significant inhibition of tubulin polymerization, and showed low IC50 values against prostate cancer cells. Flow cytometry and Western blotting assays revealed that LCE induced apoptosis, as evidenced by the increased expression of apoptotic protein-cleaved caspase-3 and the populations of early and late apoptotic cells. In the xenograft tumor experiments, LCE significantly suppressed tumor growth and reduced the tumor volume (PC3: 43.9%; Du145: 49.2%) and weight (PC3: 48.8%; Du145: 7.8%). Additionally, LCE inhibited prostate cancer cell migration, and invasion upregulated the epithelial marker E-cadherin and suppressed EMT-related proteins. Furthermore, LCE effectively attenuated TGF-ß-induced EMT in PC3 and Du145 cells. Bioactivity-guided fractionation and SwissADME validation confirmed that LCE's main component, 13-acetoxysarcocrassolide (13-AC), holds greater potential for the development of anticancer drugs.

Rest-Activity Rhythm Differences in Acute Rehabilitation Between Poststroke Patients and Non-Brain Disease Controls: Comparative Study.

BACKGROUND: Circadian rhythm disruptions are a common concern for poststroke patients undergoing rehabilitation and might negatively impact their functional outcomes. OBJECTIVE: Our research aimed to uncover unique patterns and disruptions specific to poststroke rehabilitation patients and identify potential differences in specific rest-activity rhythm indicators when compared to inpatient controls with non-brain-related lesions, such as patients with spinal cord injuries. METHODS: We obtained a 7-day recording with a wearable actigraphy device from 25 poststroke patients (n=9, 36% women; median age 56, IQR 46-71) and 25 age- and gender-matched inpatient control participants (n=15, 60% women; median age 57, IQR 46.5-68.5). To assess circadian rhythm, we used a nonparametric method to calculate key rest-activity rhythm indicators-relative amplitude, interdaily stability, and intradaily variability. Relative amplitude, quantifying rest-activity rhythm amplitude while considering daily variations and unbalanced amplitudes, was calculated as the ratio of the difference between the most active 10 continuous hours and the least active 5 continuous hours to the sum of these 10 and 5 continuous hours. We also examined the clinical correlations between rest-activity rhythm indicators and delirium screening tools, such as the 4 A's Test and the Barthel Index, which assess delirium and activities of daily living. RESULTS: Patients who had a stroke had higher least active 5-hour values compared to the control group (median 4.29, IQR 2.88-6.49 vs median 1.84, IQR 0.67-4.34; P=.008). The most active 10-hour values showed no significant differences between the groups (stroke group: median 38.92, IQR 14.60-40.87; control group: median 31.18, IQR 18.02-46.84; P=.93). The stroke group presented a lower relative amplitude compared to the control group (median 0.74, IQR 0.57-0.85 vs median 0.88, IQR 0.71-0.96; P=.009). Further analysis revealed no significant differences in other rest-activity rhythm metrics between the two groups. Among the patients who had a stroke, a negative correlation was observed between the 4 A's Test scores and relative amplitude (ρ=-0.41; P=.045). Across all participants, positive correlations emerged between the Barthel Index scores and both interdaily stability (ρ=0.34; P=.02) and the most active 10-hour value (ρ=0.42; P=.002). CONCLUSIONS: This study highlights the relevance of circadian rhythm disruptions in poststroke rehabilitation and provides insights into potential diagnostic and prognostic implications for rest-activity rhythm indicators as digital biomarkers.

Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study.

BACKGROUND: This study aimed to investigate the relationships between adiposity and circadian rhythm and compare the measurement of circadian rhythm using both actigraphy and a smartphone app that tracks human-smartphone interactions. OBJECTIVE: We hypothesized that the app-based measurement may provide more comprehensive information, including light-sensitive melatonin secretion and social rhythm, and have stronger correlations with adiposity indicators. METHODS: We enrolled a total of 78 participants (mean age 41.5, SD 9.9 years; 46/78, 59% women) from both an obesity outpatient clinic and a workplace health promotion program. All participants (n=29 with obesity, n=16 overweight, and n=33 controls) were required to wear a wrist actigraphy device and install the Rhythm app for a minimum of 4 weeks, contributing to a total of 2182 person-days of data collection. The Rhythm app estimates sleep and circadian rhythm indicators by tracking human-smartphone interactions, which correspond to actigraphy. We examined the correlations between adiposity indices and sleep and circadian rhythm indicators, including sleep time, chronotype, and regularity of circadian rhythm, while controlling for physical activity level, age, and gender. RESULTS: Sleep onset and wake time measurements did not differ significantly between the app and actigraphy; however, wake after sleep onset was longer (13.5, SD 19.5 minutes) with the app, resulting in a longer actigraphy-measured total sleep time (TST) of 20.2 (SD 66.7) minutes. The obesity group had a significantly longer TST with both methods. App-measured circadian rhythm indicators were significantly lower than their actigraphy-measured counterparts. The obesity group had significantly lower interdaily stability (IS) than the control group with both methods. The multivariable-adjusted model revealed a negative correlation between BMI and app-measured IS (P=.007). Body fat percentage (BF%) and visceral adipose tissue area (VAT) showed significant correlations with both app-measured IS and actigraphy-measured IS. The app-measured midpoint of sleep showed a positive correlation with both BF% and VAT. Actigraphy-measured TST exhibited a positive correlation with BMI, VAT, and BF%, while no significant correlation was found between app-measured TST and either BMI, VAT, or BF%. CONCLUSIONS: Our findings suggest that IS is strongly correlated with various adiposity indicators. Further exploration of the role of circadian rhythm, particularly measured through human-smartphone interactions, in obesity prevention could be warranted.

Quantification of Resection Margin following Sublobar Resection in Lung Cancer Patients through Pre- and Post-Operative CT Image Comparison: Utilizing a CT-Based 3D Reconstruction Algorithm.

Sublobar resection has emerged as a standard treatment option for early-stage peripheral non-small cell lung cancer. Achieving an adequate resection margin is crucial to prevent local tumor recurrence. However, gross measurement of the resection margin may lack accuracy due to the elasticity of lung tissue and interobserver variability. Therefore, this study aimed to develop an objective measurement method, the CT-based 3D reconstruction algorithm, to quantify the resection margin following sublobar resection in lung cancer patients through pre- and post-operative CT image comparison. An automated subvascular matching technique was first developed to ensure accuracy and reproducibility in the matching process. Following the extraction of matched feature points, another key technique involves calculating the displacement field within the image. This is particularly important for mapping discontinuous deformation fields around the surgical resection area. A transformation based on thin-plate spline is used for medical image registration. Upon completing the final step of image registration, the distance at the resection margin was measured. After developing the CT-based 3D reconstruction algorithm, we included 12 cases for resection margin distance measurement, comprising 4 right middle lobectomies, 6 segmentectomies, and 2 wedge resections. The outcomes obtained with our method revealed that the target registration error for all cases was less than 2.5 mm. Our method demonstrated the feasibility of measuring the resection margin following sublobar resection in lung cancer patients through pre- and post-operative CT image comparison. Further validation with a multicenter, large cohort, and analysis of clinical outcome correlation is necessary in future studies.

Visual Acuity Impairment from Inflammatory Isolated Sphenoid Sinus Disease.

BACKGROUND/AIM: To investigate the treatment outcomes and determinants of prognosis in patients experiencing visual acuity (VA) deterioration due to inflammatory isolated sphenoid sinus disease (ISSD) who underwent endonasal endoscopic surgery (EES). PATIENTS AND METHODS: Thirteen patients with 14 lesions treated with EES between March 2010 and April 2022 were included. Evaluation included improvements in VA using the logarithm of the minimum angle of resolution (LogMAR) scale, resolution rates of associated symptoms, and identification of factors predicting VA recovery. A literature review was conducted to assess the outcomes for ISSD-related VA impairments. RESULTS: The most common etiology is mycetoma (n=5), followed by an equal representation of mucocele and sphenoiditis (n=4). The mean interval from symptom onset to intervention was 4.7 months, with an average follow-up duration of 14.4 months. Seven eyes exhibited preoperative VA of 2.1 LogMAR or worse, with diplopia/ptosis (n=8) and headache (n=5) being the predominant co-occurring symptoms. After surgery, all ancillary symptoms improved, with an overall VA recovery rate of 87.5% (improvement more than 0.2 logMAR units). Mucocele exhibited the best improvements, whereas sphenoiditis showed the least progress (p=0.021). Poor baseline VA (p=0.026) and combined diplopia/ptosis (p=0.029) were identified as negative prognostic factors for VA recovery. CONCLUSION: Our findings suggest a favorable prognosis for VA recovery following EES in patients with inflammatory ISSDs, with response variations based on disease entity. However, further research is needed to personalize therapeutic strategies for enhanced outcomes.

Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities.

BACKGROUND: Human biological rhythms are commonly assessed through physical activity (PA) measurement, but mental activity may offer a more substantial reflection of human biological rhythms. OBJECTIVE: This study proposes a novel approach based on human-smartphone interaction to compute mental activity, encompassing general mental activity (GMA) and working mental activity (WMA). METHODS: A total of 24 health care professionals participated, wearing wrist actigraphy devices and using the "Staff Hours" app for more than 457 person-days, including 332 workdays and 125 nonworkdays. PA was measured using actigraphy, while GMA and WMA were assessed based on patterns of smartphone interactions. To model WMA, machine learning techniques such as extreme gradient boosting and convolutional neural networks were applied, using human-smartphone interaction patterns and GPS-defined work hours. The data were organized by date and divided into person-days, with an 80:20 split for training and testing data sets to minimize overfitting and maximize model robustness. The study also adopted the M10 metric to quantify daily activity levels by calculating the average acceleration during the 10-hour period of highest activity each day, which facilitated the assessment of the interrelations between PA, GMA, and WMA and sleep indicators. Phase differences, such as those between PA and GMA, were defined using a second-order Butterworth filter and Hilbert transform to extract and calculate circadian rhythms and instantaneous phases. This calculation involved subtracting the phase of the reference signal from that of the target signal and averaging these differences to provide a stable and clear measure of the phase relationship between the signals. Additionally, multilevel modeling explored associations between sleep indicators (total sleep time, midpoint of sleep) and next-day activity levels, accounting for the data's nested structure. RESULTS: Significant differences in activity levels were noted between workdays and nonworkdays, with WMA occurring approximately 1.08 hours earlier than PA during workdays (P<.001). Conversely, GMA was observed to commence about 1.22 hours later than PA (P<.001). Furthermore, a significant negative correlation was identified between the activity level of WMA and the previous night's midpoint of sleep (ß=-0.263, P<.001), indicating that later bedtimes and wake times were linked to reduced activity levels in WMA the following day. However, there was no significant correlation between WMA's activity levels and total sleep time. Similarly, no significant correlations were found between the activity levels of PA and GMA and sleep indicators from the previous night. CONCLUSIONS: This study significantly advances the understanding of human biological rhythms by developing and highlighting GMA and WMA as key indicators, derived from human-smartphone interactions. These findings offer novel insights into how mental activities, alongside PA, are intricately linked to sleep patterns, emphasizing the potential of GMA and WMA in behavioral and health studies.

Liver cancer initiation requires translational activation by an oncofetal regulon involving LIN28 proteins.

It is unknown which posttranscriptional regulatory mechanisms are required for oncogenic competence. Here, we show that the LIN28 family of RNA-binding proteins (RBPs), which facilitate posttranscriptional RNA metabolism within ribonucleoprotein networks, is essential for the initiation of diverse oncotypes of hepatocellular carcinoma (HCC). In HCC models driven by NRASG12V/Tp53, CTNNB1/YAP/Tp53, or AKT/Tp53, mice without Lin28a and Lin28b were markedly impaired in cancer initiation. We biochemically defined an oncofetal regulon of 15 factors connected to LIN28 through direct mRNA and protein interactions. Interestingly, all were RBPs and only 1 of 15 was a Let-7 target. Polysome profiling and reporter assays showed that LIN28B directly increased the translation of 8 of these 15 RBPs. As expected, overexpression of LIN28B and IGFBP1-3 was able to genetically rescue cancer initiation. Using this platform to probe components downstream of LIN28, we found that 8 target RBPs were able to restore NRASG12V/Tp53 cancer formation in Lin28a/Lin28b-deficient mice. Furthermore, these LIN28B targets promote cancer initiation through an increase in protein synthesis. LIN28B, central to an RNP regulon that increases translation of RBPs, is important for tumor initiation in the liver.

Enhancing DSSCs and Photocatalytic Hydrogen Production with D-A1-A2-π-A Sensitizers Containing 10'H-Spiro[fluorene-9,9'-phenanthren]-10'-one and Benzo[c][1,2,5]thiadiazole.

Novel D-A1-A2-π-A organic sensitizers (FZ-sensitizer), utilizing spiro[fluorene-9,9'-phenanthren]-10'-one (A1) and benzo[c][1,2,5]thiadiazole(A2) moiety as two auxiliary acceptors, are synthesized and applied in dye-sensitized solar cells (DSSCs) and hydrogen production. By incorporating a bulky A1 and A2 between the donor (D) and π-bridge moiety, structural modifications inhibit molecular aggregation, while the carbonyl group enhances the capture of Li+ ions, thereby delaying charge recombination. Furthermore, the extended π-conjugation broadens the light absorption range and enhances the power conversion efficiency (PCE) of FZ-2 under AM1.5 conditions, achieving up to 5.72%. Co-sensitization with N719 and FZ-2 shows PCE of 9.60% under one sun. Under TL84 indoor light conditions, the efficiency is 29.69% at 2500 lux. FZ-sensitizers also exhibit high efficiency in photocatalytic hydrogen production. The hydrogen production activities of FZ-2 are 9190 µmol/g (1 hour) and 76582 µmol/g (12 hours) respectively, while those of FZ-1 are 7430 µmol/g (1 hour) and 64004 µmol/g (12 hours), indicating that FZ-2 can inject charges into TiO2 more efficiently and utilize them for water splitting. Stability testing of photocatalytic water splitting after 12 hours shows a turnover number (TON) of 4249 for FZ-1 and 5378 for FZ-2.

Metabolic inflexibility promotes mitochondrial health during liver regeneration.

Mitochondria are critical for proper organ function and mechanisms to promote mitochondrial health during regeneration would benefit tissue homeostasis. We report that during liver regeneration, proliferation is suppressed in electron transport chain (ETC)-dysfunctional hepatocytes due to an inability to generate acetyl-CoA from peripheral fatty acids through mitochondrial ß-oxidation. Alternative modes for acetyl-CoA production from pyruvate or acetate are suppressed in the setting of ETC dysfunction. This metabolic inflexibility forces a dependence on ETC-functional mitochondria and restoring acetyl-CoA production from pyruvate is sufficient to allow ETC-dysfunctional hepatocytes to proliferate. We propose that metabolic inflexibility within hepatocytes can be advantageous by limiting the expansion of ETC-dysfunctional cells.

Association of Childhood Attention Deficit/Hyperactivity Disorder With Adolescent Traditional and Cyberbullying Victimization in the Digital Generation: A Population-Based Cohort Study.

PURPOSE: This population-based cohort study aimed to examine the association with childhood attention deficit/hyperactivity disorder (ADHD) and bullying experiences during adolescence among Digital Generation individuals, exploring both traditional and cyberbullying. METHODS: This study included data from 15,240 participants, collected from the Taiwan Adolescent to Adult Longitudinal Study project. Participants, initially in seventh and 10th grade in 2015, were selected through a multistage stratified sampling approach. Self-report questionnaires assessed traditional and cyberbullying victimization experiences during adolescence, with 5-year longitudinal follow-up. Childhood ADHD diagnoses were identified by linking data to Taiwan's National Health Insurance Research Database from 2000 to 2015. Logistic regression models were employed to examine the relationship between childhood ADHD and bullying victimization while controlling for relevant covariates. RESULTS: Individuals diagnosed with childhood ADHD exhibited a significantly higher likelihood of experiencing bullying during adolescence (adjusted odds ratio (aOR) = 1.52, 95% confidence interval (CI): 1.28-1.80). This association extended to various forms of bullying, including physical (aOR = 1.42, 95% CI: 1.20-1.68), verbal (aOR = 1.42, 95% CI: 1.20-1.67), relational (aOR = 1.45, 95% CI: 1.22-1.71), and cyber (aOR = 1.35, 95% CI: 1.14-1.61). Additional factors positively associated with bullying victimization included male, binge drinking, and depression, while a positive campus atmosphere was protective against bullying. However, there is no evidence for interactions between these factors and ADHD in their associations with bullying. DISCUSSION: Childhood ADHD increases the risk of both traditional and cyberbullying during adolescence. Recognizing this risk is essential for targeted interventions and further research on underlying mechanisms.

The greatest loss of unpleasant smells may be related to the risk of more severe PD symptoms.

Background: Limited research has explored the relationship between the valence of olfactory dysfunction and PD clinical symptoms. This study aimed to investigate correlations between the emotional valence of olfactory impairment and different domains of PD symptoms. Methods: PD patients who fulfilled the clinically probable PD diagnostic criteria of the International Parkinson and Movement Disorder Society Clinical Diagnostic Criteria for Parkinson's Disease were recruited from the Center for Parkinson and Movement Disorders at Taichung Veterans General Hospital between October 2016 and April 2022. Demographic data and serial clinical assessments were collected, including the traditional Chinese version of the University of Pennsylvania Smell Identification Test (UPSIT-TC) and Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Thirty-five odors from the UPSIT-TC were classified into neutral, pleasant or unpleasant groups. Group comparisons, correlation analyses, and linear regression analyses were conducted to examine the relationship between olfactory impairment of UPSIT-TC odors, considering emotional valence, and MDS-UPDRS subscores across various domains. Results: A total of 176 PD patients were recruited for analysis. Patients in the predominantly neutral/unpleasant odor impairment groups had higher MDS-UPDRS part III scores compared to those in the predominantly pleasant odor impairment group (pleasant vs. neutral vs. unpleasant odor impairment groups: 26.79 ± 13.59 vs. 35.33 ± 16.36 vs. 31.57 ± 12.37, p = 0.009). This trend was also noted in MDS-UPDRS rigidity, bradykinesia, and akinetic-rigid subscores (p = 0.003, p = 0.012, and p = 0.001, respectively). Correlation analysis revealed a weak but significant correlation between rigidity/akinetic-rigid subscores and misidentification numbers for neutral/unpleasant odors (all p < 0.05), with age, gender, LEDD, and disease duration as covariates. All significances were retained in the linear regression analysis. Conclusion: Our results emphasize the link between olfactory impairment of specific emotional valence, neutral/unpleasant odors, and PD severity, particularly with respect to akinetic-rigid symptoms. A concise olfactory test that focuses on both neutral and unpleasant odors may offer deeper insights into PD symptoms.

Suppression of the alpha, delta, and omicron variants of SARS-Cov-2 in Taiwan.

BACKGROUND: Taiwan was a coronavirus disease 2019 (COVID-19) outlier, with an extraordinarily long transmission-free record: 253 days without locally transmitted infections while the rest of the world battled wave after wave of infection. The appearance of the alpha variant in May 2021, closely followed by the delta variant, disrupted this transmission-free streak. However, despite low vaccination coverage (<1%), outbreaks were well-controlled. METHODS: This study analyzed the time to border closure and conducted one-sample t test to compare between Taiwan and Non-Taiwan countries prior to vaccine introduction. The study also collected case data to observe the dynamics of omicron transmission. Time-varying reproduction number,Rt, was calculated and was used to reflect infection impact at specified time points and model trends of future incidence. RESULTS: The study analyzed and compare the time to border closure in Taiwan and non-Taiwan countries. The mean times to any border closure from the first domestic case within each country were -21 and 5.98 days, respectively (P < .0001). The Taiwanese government invested in quick and effective contact tracing with a precise quarantine strategy in lieu of a strict lockdown. Residents followed recommendations based on self-discipline and unity. The self-discipline in action is evidenced in Google mobility reports. The central and local governments worked together to enact non-pharmaceutical interventions (NPIs), including universal masking, social distancing, limited unnecessary gatherings, systematic contact tracing, and enhanced quarantine measures. The people cooperated actively with pandemic-prevention regulations, including vaccination and preventive NPIs. CONCLUSIONS: This article describes four key factors underlying Taiwan's success in controlling COVID-19 transmission: quick responses; effective control measures with new technologies and rolling knowledge updates; unity and cooperation among Taiwanese government agencies, private companies and organizations, and individual citizens; and Taiwanese self-discipline.

Unplanned Emergency Department Visits Following Revision Total Joint Arthroplasty: Incidences, Risk Factors, and Mortalities.

BACKGROUND: The incidence of unplanned emergency department (ED) visits following revision total joint arthroplasty is an indicator of the quality of postoperative care. The aim of this study was to investigate the incidences, timings, and characteristics of ED visits within 90 days after revision total joint arthroplasty. METHODS: A retrospective review of 457 consecutive cases, including 254 revision total hip arthroplasty (rTHA) and 203 revision total knee arthroplasty (rTKA) cases, was conducted. Data regarding patient demographics, timings of the ED encounter, chief complaints, readmissions, and diagnoses indicating reoperation were analyzed. RESULTS: The results showed that 41 patients who had rTHA (16.1%) and 14 patients who had rTKA (6.9%) returned to the ED within 90 days postoperatively. The incidence of ED visits was significantly higher in the rTHA group than in the rTKA group (P = .003). The most common surgery-related complications were dislocation among rTHA patients and wound conditions among rTKA patients. Apart from elevated calculated comorbidity scores, peptic ulcer in rTHA patients and cerebral vascular events and chronic obstructive pulmonary disease in rTKA patients might increase chances of unplanned ED visits. Patients who had ED visits showed significantly higher mortality rates than the others in both rTHA and rTKA cohorts (P = .050 and P = .008, respectively). CONCLUSIONS: The ED visits within 90 days are more common after rTHA than after rTKA. Patients in both ED visit groups after rTHA and rTKA demonstrated worse survival. Efforts should be made to improve quality of care to prevent ED visits.

Examining Human-Smartphone Interaction as a Proxy for Circadian Rhythm in Patients With Insomnia: Cross-Sectional Study.

BACKGROUND: The sleep and circadian rhythm patterns associated with smartphone use, which are influenced by mental activities, might be closely linked to sleep quality and depressive symptoms, similar to the conventional actigraphy-based assessments of physical activity. OBJECTIVE: The primary objective of this study was to develop app-defined circadian rhythm and sleep indicators and compare them with actigraphy-derived measures. Additionally, we aimed to explore the clinical correlations of these indicators in individuals with insomnia and healthy controls. METHODS: The mobile app "Rhythm" was developed to record smartphone use time stamps and calculate circadian rhythms in 33 patients with insomnia and 33 age- and gender-matched healthy controls, totaling 2097 person-days. Simultaneously, we used standard actigraphy to quantify participants' sleep-wake cycles. Sleep indicators included sleep onset, wake time (WT), wake after sleep onset (WASO), and the number of awakenings (NAWK). Circadian rhythm metrics quantified the relative amplitude, interdaily stability, and intradaily variability based on either smartphone use or physical activity data. RESULTS: Comparisons between app-defined and actigraphy-defined sleep onsets, WTs, total sleep times, and NAWK did not reveal any significant differences (all P>.05). Both app-defined and actigraphy-defined sleep indicators successfully captured clinical features of insomnia, indicating prolonged WASO, increased NAWK, and delayed sleep onset and WT in patients with insomnia compared with healthy controls. The Pittsburgh Sleep Quality Index scores were positively correlated with WASO and NAWK, regardless of whether they were measured by the app or actigraphy. Depressive symptom scores were positively correlated with app-defined intradaily variability (ß=9.786, SD 3.756; P=.01) and negatively correlated with actigraphy-based relative amplitude (ß=-21.693, SD 8.214; P=.01), indicating disrupted circadian rhythmicity in individuals with depression. However, depressive symptom scores were negatively correlated with actigraphy-based intradaily variability (ß=-7.877, SD 3.110; P=.01) and not significantly correlated with app-defined relative amplitude (ß=-3.859, SD 12.352; P=.76). CONCLUSIONS: This study highlights the potential of smartphone-derived sleep and circadian rhythms as digital biomarkers, complementing standard actigraphy indicators. Although significant correlations with clinical manifestations of insomnia were observed, limitations in the evidence and the need for further research on predictive utility should be considered. Nonetheless, smartphone data hold promise for enhancing sleep monitoring and mental health assessments in digital health research.

Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study.

BACKGROUND: Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work. OBJECTIVE: In this study, we aimed to develop a novel approach called "probability in work mode," which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours. METHODS: To capture human-smartphone interactions and GPS locations, we used the "Staff Hours" app, developed by our team, to passively and continuously record participants' screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work. RESULTS: Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes. CONCLUSIONS: Our novel approach, the probability in work mode, harnessed human-smartphone interaction patterns and machine learning models to enhance the precision and accuracy of work hour investigation. By integrating human-smartphone interactions and GPS data, our method provides valuable insights into work patterns, including remote work and breaks, offering potential applications in optimizing work productivity and well-being.