Differential predictors of early- and delayed-onset post-traumatic stress disorder following physical injury: a two-year longitudinal study.

Objectives: This study aimed to investigate the predictors of both early- and delayed-onset PTSD over a 2-year period following physical injuries. Methods: Patients were recruited from a trauma center at a university hospital in South Korea (June 2015 ~ January 2021). At baseline, 1142 patients underwent comprehensive assessments including socio-demographic, pre-trauma, trauma-related, and peri-trauma evaluations. Diagnoses of acute stress disorder (ASD) and subthreshold ASD were also determined using the Clinician-administered PTSD Scale (CAPS). Follow-up assessments at three months included diagnoses of PTSD and subthreshold PTSD using CAPS, and stressful life events (SLEs), with additional evaluations at 6, 12, and 24 months. The analyzed sample comprised 1014 patients followed up at least once after the baseline and 3-month evaluations. PTSD diagnoses were categorized into early-onset (within the first six months after trauma) and delayed-onset (more than six months after trauma). Logistic regression models identified predictors for each group. Results: Early-onset and delayed-onset PTSD were diagnosed in 79 and 35 patients, respectively. Early-onset PTSD was predicted by previous psychiatric disorders, previous traumatic events, ASD and subthreshold ASD diagnoses, and higher anxiety levels. In contrast, delayed-onset PTSD was linked to higher education, higher injury severity, and subthreshold PTSD and SLEs at 3-month follow-up. Conclusion: Distinct predictors were found for early-onset and delayed-onset PTSD. The findings underscore the heterogeneous factors influencing the temporal development of PTSD post-trauma, and may provide valuable guidance for more targeted interventions and improved patient outcomes.

Design and validation of a wearable dynamometry system for knee extension-flexion torque measurement.

Muscle strength assessments are vital in rehabilitation, orthopedics, and sports medicine. However, current methods used in clinical settings, such as manual muscle testing and hand-held dynamometers, often lack reliability, and isokinetic dynamometers (IKD), while reliable, are not easily portable. The aim of this study was to design and validate a wearable dynamometry system with high accessibility, accuracy, and reliability, and to validate the device. Therefore, we designed a wearable dynamometry system (WDS) equipped with knee joint torque sensors. To validate this WDS, we measured knee extension and flexion strength in 39 healthy adults using both the IKD and WDS. Comparing maximal isometric torque measurements, WDS and IKD showed strong correlation and good reliability for extension (Pearson's r: 0.900; intraclass correlation coefficient [ICC]: 0.893; standard error of measurement [SEM]: 9.85%; minimal detectable change [MDC]: 27.31%) and flexion (Pearson's r: 0.870; ICC: 0.857; SEM: 11.93%; MDC: 33.07%). WDS demonstrated excellent inter-rater (Pearson's r: 0.990; ICC: 0.993; SEM: 4.05%) and test-retest (Pearson's r: 0.970; ICC: 0.984; SEM: 6.15%) reliability during extension/flexion. User feedback from 35 participants, including healthcare professionals, underscores WDS's positive user experience and clinical potential. The proposed WDS is a suitable alternative to IKD, providing high accuracy, reliability, and potentially greater accessibility.

Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation.

This study developed a new convolutional neural network model to detect and classify gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light endoscopy images from 2606 patients in an 8:1:1 ratio. Lesions were categorized as early gastric cancer (EGC), advanced gastric cancer (AGC), gastric dysplasia, benign gastric ulcer (BGU), benign polyp, and benign erosion. We assessed the lesion detection and classification model using six-class, cancer versus non-cancer, and neoplasm versus non-neoplasm categories, as well as T-stage estimation in cancer lesions (T1, T2-T4). The lesion detection rate was 95.22% (219/230 patients) on a per-patient basis: 100% for EGC, 97.22% for AGC, 96.49% for dysplasia, 75.00% for BGU, 97.22% for benign polyps, and 80.49% for benign erosion. The six-class category exhibited an accuracy of 73.43%, sensitivity of 80.90%, specificity of 83.32%, positive predictive value (PPV) of 73.68%, and negative predictive value (NPV) of 88.53%. The sensitivity and NPV were 78.62% and 88.57% for the cancer versus non-cancer category, and 83.26% and 89.80% for the neoplasm versus non-neoplasm category, respectively. The T stage estimation model achieved an accuracy of 85.17%, sensitivity of 88.68%, specificity of 79.81%, PPV of 87.04%, and NPV of 82.18%. The novel CNN-based model remarkably detected and classified malignant, premalignant, and benign gastric lesions and accurately estimated gastric cancer T-stages.

Brain Derived Neurotrophic Factor Methylation and Long-term Outcomes after Stroke Interacting with Suicidal Ideation.

Objective: This study aimed to evaluate the unexplored relationship between BDNF methylation, long-term outcomes, and its interaction with suicidal ideation (SI), which is closely associated with both BDNF expression and stroke outcomes. Methods: A total of 278 stroke patients were assessed for BDNF methylation status and SI using suicide-related item in the Montgomery-Åsberg Depression Rating Scale at 2 weeks post-stroke. We investigated the incidence of composite cerebro-cardiovascular events (CCVEs) during an 8-14-year period after the initial stroke as long-term stroke outcome. We conducted Cox regression models adjusted for covariates to evaluate the association between BDNF methylation status and CCVEs, as well as its interaction with post-stroke SI at 2 weeks. Results: Higher methylation status of CpG 1, 3, and 5, but not the average value, predicted a greater number of composite CCVEs during 8-14 years following the stroke. The associations between a higher methylation status of CpGs 1, 3, 5, and 8, as well as the average BDNF methylation value, and a greater number of composite CCVEs, were prominent in patients who had post-stroke SI at 2 weeks. Notably, a significant interaction between methylation status and SI on composite CCVEs was observed only for CpG 8. Conclusion: The significant association between BDNF methylation and poor long-term stroke outcomes, particularly amplified in individuals who had post-stroke SI at 2 weeks, suggested that evaluating the biological marker status of BDNF methylation along with assessing SI during the acute phase of stroke can help predict long-term outcomes.

Moderating role of functioning level on the association between suicidal ideation and delayed-onset post-traumatic stress disorder.

OBJECTIVES: This study aimed to investigate the relationship between suicidal ideation at baseline and the development of post-traumatic stress disorder (PTSD) in individuals who have experienced physical injuries, with a specific focus on how this relationship is moderated by the patient's functioning level. METHODS: Participants were consecutively recruited from a trauma center and prospectively followed for two years. At baseline, suicidal ideation was assessed using the Brief Psychiatric Rating Scale, and functioning level was evaluated using the Social and Occupational Functioning Assessment Scale. During the follow-up, PTSD diagnosis was established using the Clinician-Administered PTSD Scale for DSM-5. Binary and multinomial logistic regression analyses were employed to examine the associations between suicidal ideation, functioning level, and PTSD. RESULTS: Of the 1014 participants analyzed, 114 (11.2%) developed PTSD, with early-onset observed in 79 (7.8%) and delayed-onset in 35 (3.5%) cases. Suicidal ideation at baseline was significantly associated with both early- and delayed-onset PTSD. Notably, higher functioning individuals with baseline suicidal ideation had an increased likelihood of developing delayed-onset PTSD, while this association was not significant in lower functioning individuals, with significant interaction terms. Additionally, suicidal ideation was a consistent predictor of early-onset PTSD across all functioning levels. CONCLUSION: The impact of baseline suicidal ideation on PTSD varies depending on the individual's functioning level, with higher functioning individuals being more vulnerable to delayed-onset PTSD. These findings underscore the importance of considering functional status in the assessment and intervention of PTSD following physical trauma.

Understanding the Fluctuations in Korea's Suicide Rates: A Change-Point Analysis and Interrupted Time Series Analysis.

BACKGROUND: Korea has witnessed significant fluctuations in its suicide rates in recent decades, which may be related to modifications in its death registration system. This study aimed to explore the structural shifts in suicide trends, as well as accidental and ill-defined deaths in Korea, and to analyze the patterns of these changes. METHODS: We analyzed age-adjusted death rates for suicides, deaths due to transport accidents, falls, drowning, fire-related incidents, poisonings, other external causes, and ill-defined deaths in Korea from 1997 to 2021. We identified change-points using the 'breakpoints' function from the 'strucchange' package and conducted interrupted time series analyses to assess trends before and after these change-points. RESULTS: Korea's suicide rates had three change-points in February 2003, September 2008, and June 2012, characterized by stair-step changes, with level jumps at the 2003 and 2008 change-points and a sharp decline at the 2012 change-point. Notably, the 2003 and 2008 spikes roughly coincided with modifications to the death ascertainment process. The trend in suicide rates showed a downward slope within the 2003-2008 and 2008-2012 periods. Furthermore, ill-defined deaths and most accidental deaths decreased rapidly through several change-points in the early and mid-2000s. CONCLUSION: The marked fluctuations in Korea's suicide rate during the 2000s may be largely attributed to improvements in suicide classification, with potential implications beyond socio-economic factors. These findings suggest that the actual prevalence of suicides in Korea in the 2000s might have been considerably higher than officially reported.

Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case-Control Matched Analysis.

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.

Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis.

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.

Identification of depression in patients with acute coronary syndrome using multiple serum biomarkers.

BACKGROUND: Biomarkers for depression in patients with acute coronary syndrome (ACS) have not been identified. METHODS: This study evaluated multiple serum biomarkers for depressive disorders after ACS. Thirteen serum biomarkers associated with seven functional systems, along with sociodemographic/clinical characteristics, were evaluated in 969 patients within 2 weeks after ACS onset (acute phase). In total, 711 patients were evaluated for depressive disorder using DSM-IV criteria 1 year later (chronic phase). Logistic regression was used for the analysis. RESULTS: Depressive disorders were observed in 378 patients (39.0%) in the acute phase of ACS and 183 patients (25.7%) in the chronic phase. The weighted scores of five serum biomarkers (high-sensitivity C-reactive protein, interleukin-6, homocysteine, troponin I, and creatine kinase-MB) were significantly associated with depressive disorder diagnosis in the acute phase, and the weighted scores of three other biomarkers (tumor necrosis factor-alpha, interleukin-1 beta, and homocysteine) were significantly associated with depressive disorders in the chronic phase, in a dose-dependent manner after adjusting for relevant covariates (all P-values <0.001). CONCLUSIONS: The combination of several serum biomarkers exhibited robust associations with depressive disorders in both the acute and chronic phases of ACS.

Impact of suicidality on delayed-onset post-traumatic stress disorder is modified by age: A two-year longitudinal study.

OBJECTIVES: This study aimed to investigate the relationship between early suicidality and post-traumatic stress disorder (PTSD) onset in patients with physical injuries, focusing on age as a modifying factor. METHODS: At baseline, 1014 patients were evaluated for suicidality, age divided into younger (<60 years) vs. older (≥60 years) groups, and potential covariates. PTSD was diagnosed at follow-up at 3, 6, 12, and 24 months, and then were categorized into early-onset (within the first six months after trauma) and delayed-onset (more than six months after trauma). Logistic regression models were used after adjusting for covariates. RESULTS: Presence of suicidality at baseline was significantly associated with delayed-onset PTSD in older but not younger patients with significant interaction terms, whereas it was significantly associated with early-onset PTSD across all age groups. CONCLUSION: Age-specific associations were found between suicidality and PTSD onset. The findings highlight the importance of early suicidality assessment, especially in older patients for ongoing monitoring and support, and underscore the critical need for early PTSD identification and intervention for all ages.

Towards optimal design of patient isolation units in emergency rooms to prevent airborne virus transmission: From computational fluid dynamics to data-driven modeling.

BACKGROUND: Patient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly used for PIU design; however, optimizing this design requires extensive computational resources. Our study aims to provide data-driven models to determine the PIU settings, thereby promoting a more rapid design process. METHOD: Using CFD simulations, we evaluated various PIU parameters and room conditions to assess the impact of PIU installation on ventilation and isolation. We investigated particle dispersion from coughing subjects and airflow patterns. Machine-learning models were trained using CFD simulation data to estimate the performance and identify significant parameters. RESULTS: Physical isolation alone was insufficient to prevent the dispersion of smaller particles. However, a properly installed fan filter unit (FFU) generally enhanced the effectiveness of physical isolation. Ventilation and isolation performance under various conditions were predicted with a mean absolute percentage error of within 13%. The position of the FFU was found to be the most important factor affecting the PIU performance. CONCLUSION: Data-driven modeling based on CFD simulations can expedite the PIU design process by offering predictive capabilities and clarifying important performance factors. Reducing the time required to design a PIU is critical when a rapid response is required.

A multimodal virtual vision platform as a next-generation vision system for a surgical robot.

Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VVP can provide various critical data, including medical images, vital signs, and patient records, in three-dimensional virtual reality space so that users can access medical information simultaneously. An availability of the VVP was investigated based on various user evaluations by surgeons and novices, who executed the given tasks and answered questionnaires. The performances of the SV and VVP were not significantly different; however, the craniovertebral angle of the VVP was 16.35° higher on average than that of the SV. Survey results regarding the VVP were positive; participants indicated that the optimal number of displays was six, preferring the 2 × 3 array. Reflecting the tendencies, the VVP can be a neoconceptual candidate to be customized for medical use, which opens a new prospect in a next-generation surgical robot.

Predicting suicidal ideation using multiple serum biomarkers in patients with acute coronary syndrome.

BACKGROUND: Biomarkers for suicidal behavior in patients with acute coronary syndrome (ACS) have yet to be elucidated. This study aimed to identify a panel of serum biomarkers associated with suicidal ideation (SI) in patients with ACS. METHODS: The study evaluated 969 patients within 2 weeks of ACS (acute phase) and 711 patients 12 months later (chronic phase). The evaluation included 14 serum biomarkers covering 7 functional systems, socio-demographic/clinical characteristics, and SI assessed by the "suicidal thoughts" item of the Montgomery-Åsberg Depression Rating Scale. Logistic regression models were used to analyze the data. The results showed that 195 patients (20.1 %) had SI in the acute phase, and 87 patients (12.2 %) had SI in the chronic phase. RESULTS: A combination of five serum biomarkers (tumor necrosis factor-α, interleukin-1ß, folate, troponin I, and creatine kinase-MB) was significantly associated with SI in the acute phase, and a combination of three serum biomarkers (tumor necrosis factor-α, interleukin-1ß, and folate) was significantly associated with SI in the chronic phase in a clear dose-dependent manner (all P-values < 0.001) after adjustment for relevant covariates. DISCUSSION: These findings suggest that the application of a combination of multiple serum biomarkers could improve the predictability of SI in patients with ACS at both acute and chronic phases.

Use of Serum Biomarkers to Aid Antidepressant Selection in Depressive Patients.

Objective: : This study aimed to identify serum biomarkers prospectively associated with remission at 12 weeks in out-patients with depressive disorders receiving stepwise psychopharmacotherapy, according to the main antidepressant used during the treatment period. Methods: : This study included 1,024 depressive outpatients initially treated using antidepressant monotherapy, followed by alternating pharmacological strategies during the acute phase (3-12 weeks; 3-week interval). Fourteen serum biomarkers, sociodemographics, and clinical characteristics were evaluated at baseline. Based on the use frequency and mechanism of action, four main antidepressant types were distinguished: escitalopram, other selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), and mirtazapine. A Hamilton Depression Rating Scale score ≤ 7 was take to indicate remission. Results: : Lower high-sensitivity C-reactive protein levels were correlated with remission at 12 weeks for all antidepressant types. Lower interleukin (IL)-6 levels and tumor necrosis factor-alpha levels were associated with remission using escitalopram and other SSRIs respectively. Lower IL-1ß and leptin levels, predicted remission in association with SSRIs including escitalopram. For SNRIs, remission at 12 weeks was predicted by lower IL-4 and IL-10 levels. For mirtazapine, remission at 12 weeks was associated with lower leptin levels, and higher serotonin and folate levels. Conclusion: : Baseline serum status, as estimated by nine serum markers, may help clinicians determine the most appropriate antidepressant to achieve remission in the acute phase of depression.

Two Cases of Posttraumatic Stress Disorder Caused by a Motor Vehicle Accident Treated with Virtual Reality Exposure Therapy.

Exposure-response prevention is an effective approach to treat anxiety disorders. Virtual reality exposure therapy (VRET) is a promising treatment for patients with posttraumatic stress disorder (PTSD). New research has helped refine and update VRET. In this study, we introduce a form of VRET developed for patients suffering from PTSD after a traffic accident, and present two cases treated using this protocol. After 6 weeks of VRET treatment, the two participants not only improved their PTSD symptoms, but also improved their depressed mood, anxiety, and insomnia symptoms. Future studies of VRET for car accident-related PTSD should utilize a controlled design with randomization in order to account for numerous possible confounds.

Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning.

Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatomical characteristics of the colon pose challenges for establishing a reliable localization system. This study presents a system utilizing 100 colonoscopy videos, combining density clustering and deep learning. Cascaded CNN models are employed to estimate the appendix orifice (AO), flexures, and "outside of the body," sequentially. Subsequently, DBSCAN algorithm is applied to identify anatomical sections. Clustering-based analysis integrates clinical knowledge and context based on the anatomical section within the model. We address challenges posed by colonoscopy images through non-informative removal preprocessing. The image data is labeled by clinicians, and the system deduces section correspondence stochastically. The model categorizes the colon into three sections: right (cecum and ascending colon), middle (transverse colon), and left (descending colon, sigmoid colon, rectum). We estimated the appearance time of anatomical boundaries with an average error of 6.31 s for AO, 9.79 s for HF, 27.69 s for SF, and 3.26 s for outside of the body. The proposed method can facilitate future advancements towards AI-based automatic reporting, offering time-saving efficacy and standardization.

Case report: Periventricular heterotopia and early-onset bipolar disorder in adolescent patient with history of childhood attention deficit hyperactivity disorder.

Periventricular heterotopia (PH) is a developmental malformation in the brain. Because the clinical symptoms are heterogeneous, few studies have investigated the psychiatric symptoms associated with PH. We describe the case of a 17-year-old male with bipolar disorder (BD), who had been treated for attention deficit-hyperactivity disorder (ADHD) and developmental delay in childhood. He had experienced depression for 1 year and was admitted to the emergency room following a suicide attempt. He was admitted to the psychiatric ward for further evaluation and treatment for elated mood, decreased need for sleep, increased sexuality, and delusion. The patient was diagnosed with BP-I disorder and PH via brain magnetic resonance imaging. After combined treatment with valproic acid and aripiprazole, his manic symptoms stabilized. To our knowledge, this is the first report of an adolescent PH case with a history of early onset BD and ADHD in childhood.

Automated deep learning model for estimating intraoperative blood loss using gauze images.

The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL prediction platform, we developed an automated deep learning EBL prediction model, utilizing the patch-wise crumpled state (P-W CS) of gauze images with texture analysis. The proposed algorithm was developed using animal data obtained from a porcine experiment and validated on human intraoperative data prospectively collected from 102 laparoscopic gastric cancer surgeries. The EBL prediction model involves gauze area detection and subsequent EBL regression based on the detected areas, with each stage optimized through comparative model performance evaluations. The selected gauze detection model demonstrated a sensitivity of 96.5% and a specificity of 98.0%. Based on this detection model, the performance of EBL regression stage models was compared. Comparative evaluations revealed that our P-W CS-based model outperforms others, including one reliant on convolutional neural networks and another analyzing the gauze's overall crumpled state. The P-W CS-based model achieved a mean absolute error (MAE) of 0.25 g and a mean absolute percentage error (MAPE) of 7.26% in EBL regression. Additionally, per-patient assessment yielded an MAE of 0.58 g, indicating errors < 1 g/patient. In conclusion, our algorithm provides an objective standard and streamlined approach for EBL estimation during surgery without the need for perioperative approximation and additional tasks by humans. The robust performance of the model across varied surgical conditions emphasizes its clinical potential for real-world application.

Changes in Hip and Knee Strength Are Not Associated With Improved Clinical Outcomes After Rehabilitation in Individuals With Patellofemoral Pain: A Critically Appraised Topic.

CLINICAL SCENARIO: Patellofemoral pain (PFP) is a widespread knee disorder encountered in clinical practice. Clinicians have often focused on strengthening hip and knee musculature to improve pain and disability, which are the ultimate clinical goals of PFP treatment. However, PFP literature has shown improvement in pain and disability without concurrent changes in lower-extremity strength after rehabilitation. Although some researchers have achieved a significant increase in strength after rehabilitation in PFP cohorts, there was no association with improved pain and disability. The inconsistent improvements in strength and the lack of association with clinical outcomes call for a critical appraisal of the available evidence to better understand the association between changes in hip and knee strength and improved clinical outcomes in individuals with PFP. CLINICAL QUESTION: Are changes in hip and knee strength associated with improved pain and disability after rehabilitation in individuals with PFP? SUMMARY OF KEY FINDINGS: Four studies met the inclusion criteria and were included in the appraisal. Following rehabilitation, one study achieved strength improvements in knee extension. One study achieved strength improvements in knee extension, but not in hip external rotation and hip abduction. Two studies did not achieve strength improvements in hip external rotation, hip abduction, hip extension, or knee extension. All included studies achieved improvements in pain or disability after rehabilitation. None of the studies found a significant association between changes in hip and knee strength (either improved or not) and improved pain and disability. CLINICAL BOTTOM LINE: There is consistent evidence that changes in hip and knee strength are not associated with improved clinical outcomes after rehabilitation in adults with PFP. STRENGTH OF RECOMMENDATION: Collectively, the body of evidence included is to answer the clinical question aligns with the strength of recommendation of B based on the Strength of Recommendation Taxonomy.

The moderating role of circadian gene polymorphisms in the relationship between sleep disturbance and circulating lymphocyte subsets in colorectal cancer patients.

AIM: We investigated the impact of sleep disturbance on immune status in colorectal cancer (CRC) patients with consideration of the moderating role of circadian clock gene polymorphisms. METHODS: A prospective longitudinal study design was used to collect information regarding sleep disturbance. Blood samples for immunologic assays were obtained the day before the first (baseline) and last cycles of 5-fluorouracil, leucovorin, and oxaliplatin (FOLFOX) chemotherapy. Clinical sleep disturbance was compared between the two-time points using the Pittsburgh Sleep Quality Index (PSQI) global score. We analysed single-nucleotide polymorphisms in rs2278749, rs3749474, rs2291738, rs17031614, and rs2287161. The dependent variables included changes in the percentages of CD4+, CD8+, CD19+, and CD16/56+ lymphocytes between the two-time points. The results were analysed using moderated regression analysis; the p-values were adjusted using the false discovery rate. RESULTS: Among the 104 patients, no significant dyadic associations were observed between changes in lymphocyte percentages and the PSQI global score. However, the moderated regression analysis revealed five significant associations (rs2287161 with CD8+, rs2278749 and rs2291738 with CD19+, and rs17031614 with CD4+ and CD16/56+ lymphocytes). The inclusion of each interaction resulted in a significant increase (5.7-10.7%) in the variance explained by changes in lymphocyte percentage. CONCLUSION: Patients with specific circadian gene allele types may be more susceptible to immune dysregulation when experiencing sleep disturbances. Considering that sleep disturbance is a modifiable factor that can impact immune regulation, it is essential to prioritise the management of sleep disturbances in CRC patients receiving FOLFOX chemotherapy.