ACE2 and TMPRSS2 SARS-CoV-2 infectivity genes: deep mutational scanning and characterization of missense variants.

The human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) proteins play key roles in the cellular internalization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus responsible for the coronavirus disease of 2019 (COVID-19) pandemic. We set out to functionally characterize the ACE2 and TMPRSS2 protein abundance for variant alleles encoding these proteins that contained non-synonymous single-nucleotide polymorphisms (nsSNPs) in their open reading frames (ORFs). Specifically, a high-throughput assay, deep mutational scanning (DMS), was employed to test the functional implications of nsSNPs, which are variants of uncertain significance in these two genes. Specifically, we used a 'landing pad' system designed to quantify the protein expression for 433 nsSNPs that have been observed in the ACE2 and TMPRSS2 ORFs and found that 8 of 127 ACE2, 19 of 157 TMPRSS2 isoform 1 and 13 of 149 TMPRSS2 isoform 2 variant proteins displayed less than ~25% of the wild-type protein expression, whereas 4 ACE2 variants displayed 25% or greater increases in protein expression. As a result, we concluded that nsSNPs in genes encoding ACE2 and TMPRSS2 might potentially influence SARS-CoV-2 infectivity. These results can now be applied to DNA sequence data for patients infected with SARS-CoV-2 to determine the possible impact of patient-based DNA sequence variation on the clinical course of SARS-CoV-2 infection.

Revised ISHAM-ABPA working group clinical practice guidelines for diagnosing, classifying and treating allergic bronchopulmonary aspergillosis/mycoses.

BACKGROUND: The International Society for Human and Animal Mycology (ISHAM) working group proposed recommendations for managing allergic bronchopulmonary aspergillosis (ABPA) a decade ago. There is a need to update these recommendations due to advances in diagnostics and therapeutics. METHODS: An international expert group was convened to develop guidelines for managing ABPA (caused by Aspergillus spp.) and allergic bronchopulmonary mycosis (ABPM; caused by fungi other than Aspergillus spp.) in adults and children using a modified Delphi method (two online rounds and one in-person meeting). We defined consensus as ≥70% agreement or disagreement. The terms "recommend" and "suggest" are used when the consensus was ≥70% and <70%, respectively. RESULTS: We recommend screening for A. fumigatus sensitisation using fungus-specific IgE in all newly diagnosed asthmatic adults at tertiary care but only difficult-to-treat asthmatic children. We recommend diagnosing ABPA in those with predisposing conditions or compatible clinico-radiological presentation, with a mandatory demonstration of fungal sensitisation and serum total IgE ≥500 IU·mL-1 and two of the following: fungal-specific IgG, peripheral blood eosinophilia or suggestive imaging. ABPM is considered in those with an ABPA-like presentation but normal A. fumigatus-IgE. Additionally, diagnosing ABPM requires repeated growth of the causative fungus from sputum. We do not routinely recommend treating asymptomatic ABPA patients. We recommend oral prednisolone or itraconazole monotherapy for treating acute ABPA (newly diagnosed or exacerbation), with prednisolone and itraconazole combination only for treating recurrent ABPA exacerbations. We have devised an objective multidimensional criterion to assess treatment response. CONCLUSION: We have framed consensus guidelines for diagnosing, classifying and treating ABPA/M for patient care and research.

Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.

OBJECTIVES: Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score of ≤8 on the Montgomery Åsberg Depression Rating Scale [MADRS]). METHODS: Supervised machine learning models were trained on data from BP-D patients treated with olanzapine (N = 168) and were externally validated on patients treated with olanzapine/fluoxetine combination (OFC; N = 131) and lamotrigine (LTG; N = 126). Top predictors were used to develop a prognosis rule informing how many symptoms should change and by how much within 4 weeks to increase the odds of achieving remission. RESULTS: An AUC of 0.76 (NIR:0.59; p = 0.17) was established to predict remission in olanzapine-treated subjects. These trained models achieved AUCs of 0.70 with OFC (NIR:0.52; p < 0.03) and 0.73 with LTG (NIR:0.52; p < 0.003), demonstrating external replication of prediction performance. Week-4 changes in four MADRS symptoms (reported sadness, reduced sleep, reduced appetite, and concentration difficulties) were top predictors of remission. Across all pharmacotherapies, three or more of these symptoms needed to improve by ≥2 points at Week-4 to have a 65% chance of achieving remission at 8 weeks (OR: 3.74, 95% CI: 2.45-5.76; p < 9.3E-11). CONCLUSION: Machine learning strategies achieved cross-trial and cross-drug replication in predicting remission after 8 weeks of pharmacotherapy for BP-D. Interpretable prognoses rules required only a limited number of depressive symptoms, providing a promising foundation for developing simple quantitative decision aids that may, in the future, serve as companions to clinical judgment at the point of care.

Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review.

BACKGROUND: The occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers. OBJECTIVE: This study aims to identify physiological biomarkers of burnout, and establish what gaps are currently present in the use of wearable technologies for burnout prediction among health care professionals (HCPs). METHODS: A comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases were Ovid: MEDLINE(R), Embase, Healthstar, APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science Core Collection via Clarivate Analytics, Scopus via Elsevier, EBSCOhost: Academic Search Premier, CINAHL with Full Text, and Business Source Premier. Studies observing anxiety, burnout, stress, and depression using a wearable device worn by an HCP were included, with HCP defined as medical students, residents, physicians, and nurses. Bias was assessed using the Newcastle Ottawa Quality Assessment Form for Cohort Studies. RESULTS: The initial search yielded 505 papers, from which 10 (1.95%) studies were included in this review. The majority (n=9) used wrist-worn biosensors and described observational cohort studies (n=8), with a low risk of bias. While no physiological measures were reliably associated with burnout or anxiety, step count and time in bed were associated with depressive symptoms, and heart rate and heart rate variability were associated with acute stress. Studies were limited with long-term observations (eg, ≥12 months) and large sample sizes, with limited integration of wearable data with system-level information (eg, acuity) to predict burnout. Reporting standards were also insufficient, particularly in device adherence and sampling frequency used for physiological measurements. CONCLUSIONS: With wearables offering promise for digital health assessments of human functioning, it is possible to see wearables as a frontier for predicting burnout. Future digital health studies exploring the utility of wearable technologies for burnout prediction should address the limitations of data standardization and strategies to improve adherence and inclusivity in study participation.

The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses.

BACKGROUND: When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data. METHODS: A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly. DISCUSSION: The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices. TRIAL REGISTRATION: NCT05481138.

Population pharmacokinetic model of cefepime for critically ill adults: a comparative assessment of eGFR equations.

Cefepime exhibits highly variable pharmacokinetics in critically ill patients. The purpose of this study was to develop and qualify a population pharmacokinetic model for use in the critically ill and investigate the impact of various estimated glomerular filtration rate (eGFR) equations using creatinine, cystatin C, or both on model parameters. This was a prospective study of critically ill adults hospitalized at an academic medical center treated with intravenous cefepime. Individuals with acute kidney injury or on kidney replacement therapy or extracorporeal membrane oxygenation were excluded. A nonlinear mixed-effects population pharmacokinetic model was developed using data collected from 2018 to 2022. The 120 included individuals contributed 379 serum samples for analysis. A two-compartment pharmacokinetic model with first-order elimination best described the data. The population mean parameters (standard error) in the final model were 7.84 (0.24) L/h for CL1 and 15.6 (1.45) L for V1. Q was fixed at 7.09 L/h and V2 was fixed at 10.6 L, due to low observed interindividual variation in these parameters. The final model included weight as a covariate for volume of distribution and the eGFRcr-cysC (mL/min) as a predictor of drug clearance. In summary, a population pharmacokinetic model for cefepime was created for critically ill adults. The study demonstrated the importance of cystatin C to prediction of cefepime clearance. Cefepime dosing models which use an eGFR equation inclusive of cystatin C are likely to exhibit improved accuracy and precision compared to dosing models which incorporate an eGFR equation with only creatinine.

An Improved Recombineering Toolset for Plants.

Gene functional studies often rely on the expression of a gene of interest as transcriptional and translational fusions with specialized tags. Ideally, this is done in the native chromosomal contexts to avoid potential misexpression artifacts. Although recent improvements in genome editing have made it possible to directly modify the target genes in their native chromosomal locations, classical transgenesis is still the preferred experimental approach chosen in most gene tagging studies because of its time efficiency and accessibility. We have developed a recombineering-based tagging system that brings together the convenience of the classical transgenic approaches and the high degree of confidence in the results obtained by direct chromosomal tagging using genome-editing strategies. These simple, scalable, customizable recombineering toolsets and protocols allow a variety of genetic modifications to be generated. In addition, we developed a highly efficient recombinase-mediated cassette exchange system to facilitate the transfer of the desired sequences from a bacterial artificial chromosome clone to a transformation-compatible binary vector, expanding the use of the recombineering approaches beyond Arabidopsis (Arabidopsis thaliana). We demonstrated the utility of this system by generating more than 250 whole-gene translational fusions and 123 Arabidopsis transgenic lines corresponding to 62 auxin-related genes and characterizing the translational reporter expression patterns for 14 auxin biosynthesis genes.

Efficacy and safety of lamotrigine in pediatric mood disorders: A systematic review.

AIM: To appraise the current evidence on the efficacy and safety of lamotrigine (LAM) in the treatment of pediatric mood disorders (PMD) (i.e., Major Depressive disorder [MDD], bipolar disorder [BD]). METHODS: Major databases were searched for randomized controlled trials (RCTs), open-label trials, and observational studies reporting on pediatric (age < 18 years) patients treated with LAM for mood disorders. RESULTS: A total of 3061 abstracts were screened and seven articles were selected for inclusion. Seven studies (319 BD and 43 MDD patients), including one RCT (n = 173), three prospective (n = 105), and three retrospective (n = 84) studies, met the study criteria with a study duration range from 8 to 60.9 weeks. The mean age of this pooled data is 14.6 ± 2.0 years. LAM daily dosage varied from 12.5 to 391.3 mg/day among the studies. In an important finding, the RCT reported favorable outcomes with LAM (HR = 0.46; p = 0.02) in 13- to 17-year-old age group as compared with 10- to 12-year-old age group (HR = 0.93; p = 0.88). In addition, time to occurrence of a bipolar event trended toward favoring LAM over placebo. All the studies identified LAM as an effective and safe drug in PMDs especially, BDs. Overall, LAM was well tolerated with no major significant side effects and no cases of Stevens-Johnson syndrome. CONCLUSIONS: Most studies suggested that LAM was safe and effective in pediatric patients with mood disorders. However, the data regarding the therapeutic range for LAM are lacking. Based on the data, there is inconsistent evidence to make conclusive recommendations on therapeutic LAM dosage for mood improvement in the pediatric population. Further studies including larger sample sizes are required to address this relevant clinical question.

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.

BACKGROUND: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS: The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.

Systematic review: Wearable remote monitoring to detect nonalcohol/nonnicotine-related substance use disorder symptoms.

BACKGROUND AND OBJECTIVES: Substance use disorders (SUDs) are chronic relapsing diseases characterized by significant morbidity and mortality. Phenomenologically, patients with SUDs present with a repeating cycle of intoxication, withdrawal, and craving, significantly impacting their diagnosis and treatment. There is a need for better identification and monitoring of these disease states. Remote monitoring chronic illness with wearable devices offers a passive, unobtrusive, constant physiological data assessment. We evaluate the current evidence base for remote monitoring of nonalcohol, nonnicotine SUDs. METHODS: We performed a systematic, comprehensive literature review and screened 1942 papers. RESULTS: We found 15 studies that focused mainly on the intoxication stage of SUD. These studies used wearable sensors measuring several physiological parameters (ECG, HR, O2 , Accelerometer, EDA, temperature) and implemented study-specific algorithms to evaluate the data. DISCUSSION AND CONCLUSIONS: Studies were extracted, organized, and analyzed based on the three SUD disease states. The sample sizes were relatively small, focused primarily on the intoxication stage, had low monitoring compliance, and required significant computational power preventing "real-time" results. Cardiovascular data was the most consistently valuable data in the predictive algorithms. This review demonstrates that there is currently insufficient evidence to support remote monitoring of SUDs through wearable devices. SCIENTIFIC SIGNIFICANCE: This is the first systematic review to show the available data on wearable remote monitoring of SUD symptoms in each stage of the disease cycle. This clinically relevant approach demonstrates what we know and do not know about the remote monitoring of SUDs within disease states.

Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review.

BACKGROUND: Mental health disorders are a leading cause of medical disabilities across an individual's lifespan. This burden is particularly substantial in children and adolescents because of challenges in diagnosis and the lack of precision medicine approaches. However, the widespread adoption of wearable devices (eg, smart watches) that are conducive for artificial intelligence applications to remotely diagnose and manage psychiatric disorders in children and adolescents is promising. OBJECTIVE: This study aims to conduct a scoping review to study, characterize, and identify areas of innovations with wearable devices that can augment current in-person physician assessments to individualize diagnosis and management of psychiatric disorders in child and adolescent psychiatry. METHODS: This scoping review used information from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive search of several databases from 2011 to June 25, 2021, limited to the English language and excluding animal studies, was conducted. The databases included Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; Ovid Cochrane Database of Systematic Reviews; Web of Science; and Scopus. RESULTS: The initial search yielded 344 articles, from which 19 (5.5%) articles were left on the final source list for this scoping review. Articles were divided into three main groups as follows: studies with the main focus on autism spectrum disorder, attention-deficit/hyperactivity disorder, and internalizing disorders such as anxiety disorders. Most of the studies used either cardio-fitness chest straps with electrocardiogram sensors or wrist-worn biosensors, such as watches by Fitbit. Both allowed passive data collection of the physiological signals. CONCLUSIONS: Our scoping review found a large heterogeneity of methods and findings in artificial intelligence studies in child psychiatry. Overall, the largest gap identified in this scoping review is the lack of randomized controlled trials, as most studies available were pilot studies and feasibility trials.

Real-world experiences with acupuncture among breast cancer survivors: a cross-sectional survey study.

PURPOSE: The purpose of this study was to evaluate acupuncture use among breast cancer survivors, including perceived symptom improvements and referral patterns. METHODS: Breast cancer survivors who had used acupuncture for cancer- or treatment-related symptoms were identified using an ongoing prospective Mayo Clinic Breast Disease Registry (MCBDR). Additionally, Mayo Clinic electronic health records (MCEHR) were queried to identify eligible participants. All received a mailed consent form and survey including acupuncture-related questions about acupuncture referrals, delivery, and costs. Respondents were also asked to recall symptom severity before and after acupuncture treatment and time to benefit on Likert scales. RESULTS: Acupuncture use was reported among 415 participants (12.3%) of the MCBDR. Among MCBDR and MCEHR eligible participants, 241 women returned surveys. A total of 193 (82.1%) participants reported a symptomatic benefit from acupuncture, and 57 (24.1% of participants) reported a "substantial benefit" or "totally resolved my symptoms" (corresponding to 4 and 5 on the 5-point Likert scale). The mean symptom severity decreased by at least 1 point of the 5-point scale for each symptom; the percentage of patients who reported an improvement in symptoms ranged from 56% (lymphedema) to 79% (headache). The majority of patients reported time to benefit as "immediate" (34%) or "after a few treatments" (40.4%). Over half of the participants self-referred for treatment; 24.1% were referred by their oncologist. Acupuncture delivery was more frequent in private offices (61.0%) than in hospital or medical settings (42.3%). Twelve participants (5.1%) reported negative side effects, such as discomfort. CONCLUSIONS: Acupuncture is commonly utilized by patients for a variety of breast cancer-related symptoms. However, patients frequently self-refer for acupuncture treatments, and most acupuncture care is completed at private offices, rather than medical clinic or hospital settings.

Optimizing Value of Colon Surgery in Michigan.

OBJECTIVE: To assess the value of bundling perioperative care measures in colon surgery. BACKGROUND: Surgical site infections (SSI) in colectomy are associated with increased morbidity and cost. Perioperative care bundling has been designed to improve processes of care surrounding colectomy operations. METHODS: Retrospective cohort study performed by the Michigan Surgical Quality Collaborative (MSQC) of patients who underwent elective colon surgery from 2012 to 2015. We identified 3,387 patients in the MSQC database who underwent colon surgery. Of these cases, 332 had associated episodic cost data. RESULTS: High compliance (3-6 bundle elements) and low compliance (0-2 bundle elements) had a risk-adjusted SSI rate of 8.2% (95% confidence interval, CI, 7.2-9.2%) and 16.0% (95% CI, 12.9-19.1%), respectively (P < 0.01). When compared with low compliance, the high compliance group had an absolute risk reduction of 3.6% (P < 0.01), 2.9% (P < 0.01) and 1.3% (P < 0.01) for SSI rates in superficial space, deep space, and organ space, respectively. Low compliance had an average episodic cost of $20,046 (95% CI, $17,281-$22,812) whereas high compliance had an episodic cost of $15,272 (95% CI, $14,354-$16,192). This showed a $4,774 (95% CI, $1,859-$7,688) and 23.8% cost reduction (P < 0.01). Facility base payments decreased 14.8% ($13,444; $11,458), professional payments decreased 43.9% ($5,180; $2,906), and other payments decreased 36.2% ($1,422; $908). CONCLUSIONS: A colectomy perioperative care bundle in Michigan is associated with improved value of surgical care. We will expand efforts to implement perioperative care bundles in Michigan to improve outcomes and reduce costs.

Clinical evaluation of the periodontal health condition and oral health awareness in Parkinson's disease patients.

OBJECTIVE AND BACKGROUND: The objectives were to compare periodontal status between subjects with and without Parkinson's disease (PKD) to determine the influence of PKD on periodontal disease. This study was conducted to evaluate the relationship of periodontal status with severity of PKD. MATERIALS AND METHODS: This study was conducted on 45 subjects with PKD (subjects with PKD were divided into 5 groups from group 2 to group 6 according to Hoehn and Yahr stages) and 46 control subjects (group 1). Probing depth (PD), clinical attachment level (CAL), gingival index (GI), plaque index (PI) and percentage of bleeding sites (%BoP) were evaluated. All subjects were interviewed regarding their practice of oral hygiene and access to professional dental care. RESULTS: There were statistically significant differences in PD, CAL, GI, PI and %BoP in subjects with PKD and controls (p < 0.001). All the evaluated periodontal clinical parameters and indices deteriorate with increase in severity of PKD. The mean PD value increased from 2.75 mm for group 1 to 6.17 mm for group 6, and mean CAL value increased from 3.14 mm for group 1 to 6.74 mm for group 6. The mean GI, PI and %BoP values increased from 0.55, 1.35 and 20.37 to 2.66, 3.80 and 70.86, respectively with increasing severity of PKD. CONCLUSION: There is a need for dental care and encouragement to use plaque control methods for subjects with PKD as periodontal pathology presented a high prevalence even in the early stages of PKD.

Correlation of human S100A12 (EN-RAGE) and high-sensitivity C-reactive protein as gingival crevicular fluid and serum markers of inflammation in chronic periodontitis and type 2 diabetes.

OBJECTIVE: The aim of the present study was to evaluate the levels and correlation of human S100A12 and high-sensitivity C-reactive protein (hs-CRP) in gingival crevicular fluid (GCF) and serum in chronic periodontitis (CP) subjects with and without type 2 diabetes mellitus (DM). MATERIALS AND METHODS: A total of 44 subjects were divided into three groups: group 1 had 10 periodontally healthy subjects, group 2 consisted of 17 CP subjects and group 3 had 17 type 2 DM subjects with CP. GCF and serum levels of human S100A12 and hs-CRP were quantified using enzyme-linked immunosorbent assay and immunoturbidimetric analysis, respectively. The clinical outcomes evaluated were gingival index, probing depth and clinical attachment level and the correlations of the two inflammatory mediators with clinical parameters were evaluated. RESULTS: Both human S100A12 and hs-CRP levels increased from group 1 to group 2 to group 3. The GCF and serum values of both these inflammatory mediators correlated positively with each other and with the periodontal parameters evaluated (p < 0.05). CONCLUSION: Human S100A12 and hs-CRP can be considered as possible GCF and serum markers of inflammatory activity in CP and DM.

Association of metabolic syndrome and periodontal disease in an Indian population.

BACKGROUND: Metabolic syndrome, the whole of interconnected factors, presents with local manifestation, such as periodontitis, related by a common factor known as oxidative stress. The aim of the present study was to assess the association between metabolic syndrome and periodontal disease in an Indian population. METHODS: Clinical criteria for metabolic syndrome included 1) abdominal obesity; 2) increased triglycerides; 3) decreased high-density lipoprotein cholesterol; 4) hypertension or current use of hypertension medication; and 5) high fasting plasma glucose. Serum C-reactive protein (CRP) levels were also measured. Periodontal parameters including gingival index (GI) average and deepest probing depth (PD) and clinical attachment level (CAL) were recorded on randomly selected quadrants, one maxillary and one mandibular. Based on the presence or absence of metabolic syndrome, individuals were divided into two groups. RESULTS: The periodontal parameters PD, CAL and GI differed significantly between the two groups. The GI values in Group 1 (2.06 ± 0.57) were greater than in Group 2 (1.79 ± 0.66; p = 0.0025). Similarly PD and CAL values in Group 1 (4.58 ± 1.69 and 2.63 ± 1.61 mm) were significantly greater (p < 0.001) than in Group 2 (3.59 ± 1.61 and 1.61 ± 1.40 mm, respectively). Also, three metabolic components and serum CRP correlated with average PD, and the strength of the correlation was medium in Group 1 as compared to Group 2, in which it was weak. CONCLUSION: The association between metabolic syndrome and periodontal disease was significant, and abdominal obesity appeared to be the most important contributing metabolic factor to periodontal disease.

Machine Learning Predicts Conversion from Normal Aging to Mild Cognitive Impairment Using Medical History, APOE Genotype, and Neuropsychological Assessment.

Background: Identifying individuals at risk for mild cognitive impairment (MCI) is of urgent clinical need. Objective: This study aimed to determine whether machine learning approaches could harness longitudinal neuropsychology measures, medical data, and APOEɛ4 genotype to identify individuals at risk of MCI 1 to 2 years prior to diagnosis. Methods: Data from 676 individuals who participated in the 'APOE in the Predisposition to, Protection from and Prevention of Alzheimer's Disease' longitudinal study (N = 66 who converted to MCI) were utilized in supervised machine learning algorithms to predict conversion to MCI. Results: A random forest algorithm predicted conversion 1-2 years prior to diagnosis with 97% accuracy (p = 0.0026). The global minima (each individual's lowest score) of memory measures from the 'Rey Auditory Verbal Learning Test' and the 'Selective Reminding Test' were the strongest predictors. Conclusions: This study demonstrates the feasibility of using machine learning to identify individuals likely to convert from normal cognition to MCI.

The Association of Antidepressants in Late Pregnancy with Postpartum Hemorrhage: Systematic Review of Controlled Observational Studies.

Introduction: Despite advances in obstetric care, postpartum hemorrhage (PPH) is a leading cause of maternal mortality worldwide. Prior reviews of studies published through 2016 suggest an association of antidepressant use during late pregnancy and increased risk of PPH. However, a causal link between prenatal antidepressants and PPH remains controversial. Objectives: This systematic literature review aimed to synthesize the empirical evidence on the association of antidepressant exposure in late pregnancy with the risk of PPH, including studies published before and after 2016. Methods: A systematic literature search was conducted using PubMed, OVID Medline, EMBASE, SCOPUS, PsycINFO, and CINAHL from inception to September 9, 2023. Original, peer-reviewed studies (published in English) that reported on the frequency or risk of PPH in women with evidence of antidepressant use during pregnancy and included at least one control group were included. Results: Twenty studies (eight published after 2016) met inclusion criteria, most of which focused on the risks of PPH associated with selective serotonin reuptake inhibitors (SSRIs) or serotonin-norepinephrine reuptake inhibitors (SNRIs). The main findings from the individual studies were mixed, but the majority documented statistically significant associations of PPH with late prenatal exposure, especially for exposures occurring within 30 days of delivery, compared with unexposed deliveries. Fourteen studies addressed underlying antidepressant indications or their correlates. Few studies focused on prenatal antidepressants and the risk of well-defined severe PPH or on antidepressant dose changes and general PPH risk. None examined competing risks of antidepressant discontinuation on mental health outcomes. Conclusions: Late pregnancy exposure to antidepressants may be a minor risk factor for PPH, but it is unclear to what extent reported associations are causal in nature, as opposed to correlational (effects related to nonpharmacological factors including maternal indication). For patients needing antidepressants during pregnancy, current evidence does not favor routinely discontinuing antidepressants specifically to reduce the risk of PPH.