• Machine learning
• Posttraumatic stress disorder
• Psychotherapy
• Systematic review

• Humans
• Gene-Environment Interaction
• Precision Medicine/methods
• Dementia/genetics
• Dementia/therapy
• Genetic Predisposition to Disease
• Genomics

• Humans
• Female
• Male
• Adult
• Middle Aged
• Pharmacogenomic Testing/methods
• Pharmacogenomic Testing/statistics & numerical data
• Aged
• Polymorphism, Single Nucleotide
• Lithium/therapeutic use
• Genotype
• Calcium Channels, L-Type/genetics
• Bipolar Disorder/drug therapy
• Bipolar Disorder/genetics
• Anxiety/psychology
• Anxiety/drug therapy
• Surveys and Questionnaires

• Hidden Markov model
• machine learning
• mental health
• mental health crisis
• predictive analytics
• probabilistic modeling
• psychiatry

• outcomes research
• drug development

• Mental Health
• Psychotherapy/methods
• Precision Medicine

• UL1 TR002345 NCATS NIH HHS
• U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS)

• Artificial intelligence
• Biomarker
• Depression
• Digital phenotype
• Omics
• Organoid
• Personalized medicine
• Pharmacogenomics
• Precision medicine

• Humans
• Precision Medicine/methods
• Artificial Intelligence
• Depression/therapy
• Neoplasms/therapy
• Neoplasms/psychology
• Epilepsy/therapy
• Comorbidity

• Hidden Markov Model
• electroencephalogram
• major depressive disorder
• neuronal dynamics
• temporal encoding

• National Natural Science Foundation of China
• National Key Research and Development Program of China

• Alcohol
• Alcohol use disorder
• Resilience

• Adult
• Humans
• United States
• Resilience, Psychological
• Alcohol Drinking/prevention & control
• Alcoholism

• R01 AA030010 NIAAA NIH HHS
• T32 MH020030 NIMH NIH HHS

• psychology
• human behaviour

To investigate the risk factors for hallux valgus complicated with pain under the second metatarsal and construct an effective model and method for predicting hallux valgus complicated with pain under the second metatarsal based on risk factors.

A total of 545 patients with hallux valgus who were admitted to our hospital were divided randomly into a training set and a validation set. The demographic characteristics, imaging indices and gait test indices of the patients were collected. The risk factors were identified by univariate and multivariate logistic regression analyses. A risk prediction model for hallux valgus with pain under the second metatarsal was established, and the area under the curve (AUC) of the receiver operating characteristic and a decision curve analysis were used for verification and identification. The value of the model was tested in the verification group.

Second metatarsal length, second metatarsal peak pressure, hallux valgus angle (HVA), intermetatarsal angle 1–2 (IMA1–2) and weight were the risk factors for hallux valgus complicated with pain under the second metatarsal. Based on the weighting of these seven risk factors, a prediction model was established. The AUC of the prediction model was 0.84 (95% confidence interval [CI]: 0.802~0.898, P < 0.05), and the results of a Hosmer–Lemeshow test showed a good degree of calibration (χ² = 10.62, P > 0.05). The internal validation of the AUC was 0.83 (95% CI: 0.737–0.885, P < 0.05). The model had obvious net benefits when the threshold probability was 10%–70%.

Second metatarsal length, second metatarsal peak pressure, HVA, IMA1–2 and weight were the risk factors for hallux valgus combined with second metatarsal pain. The risk prediction model for hallux valgus complicated with pain under the second metatarsal based on these seven variables was proven effective.

Level III, retrospective comparative study.

• Beijing Municipal Science and Technology Project
• Beijing Municipal Natural Science Foundation of China

• Artificial intelligence
• Bipolar disorders
• Ethics
• Precision medicine
• Prevention
• Psychosis

• Developmental psychopathology
• Dimensional classification
• Externalising problems
• Internalising problems
• Transdiagnostic mental health

• Anxiety
• Child
• Humans
• Mental Disorders/psychology
• Mental Health
• Psychopathology
• Syndrome

• China
• attitude
• clinical pharmacist
• knowledge
• pharmacogenomic testing

• 81803497 National Natural Science Foundation of China

• 16S rRNA sequencing
• depression
• gut microbiota
• metabolomics
• treatment responses

• National Natural Science Foundation of China

Background: Despite significant research progress surrounding precision medicine in psychiatry, there has been little tangible impact upon real-world clinical care. Objective: To identify barriers and facilitators affecting the real-world implementation of precision psychiatry. Method: A PRISMA-compliant systematic literature search of primary research studies, conducted in the Web of Science, Cochrane Central Register of Controlled Trials, PsycINFO and OpenGrey databases. We included a qualitative data synthesis structured according to the ‘Consolidated Framework for Implementation Research’ (CFIR) key constructs. Results: Of 93,886 records screened, 28 studies were suitable for inclusion. The included studies reported 38 barriers and facilitators attributed to the CFIR constructs. Commonly reported barriers included: potential psychological harm to the service user (n = 11), cost and time investments (n = 9), potential economic and occupational harm to the service user (n = 8), poor accuracy and utility of the model (n = 8), and poor perceived competence in precision medicine amongst staff (n = 7). The most highly reported facilitator was the availability of adequate competence and skills training for staff (n = 7). Conclusions: Psychiatry faces widespread challenges in the implementation of precision medicine methods. Innovative solutions are required at the level of the individual and the wider system to fulfil the translational gap and impact real-world care.

• barriers
• facilitators
• precision medicine
• psychiatry
• real-world implementation
• systematic review

Cognitive biases can arise from cognitive processing under affective states and reflect the impact of emotion on cognition. In animal studies, the existing methods for detecting animal emotional state are still relatively limited, and cognitive bias test has gradually become an important supplement. In recent years, its effectiveness in animal research related to neuropsychiatric disorders has been widely verified. Some studies have found that cognitive bias test is more sensitive than traditional test methods such as forced swimming test and sucrose preference test in detecting emotional state. Therefore, it has great potential to become an important tool to measure the influence of neuropsychiatric disorder-associated emotions on cognitive processing. Moreover, it also can be used in early drug screening to effectively assess the potential effects or side effects of drugs on affective state prior to clinical trials. In this mini-review, we summarize the application of cognitive bias tests in animal models of neuropsychiatric disorders such as depression, anxiety, bipolar disorder, and pain. We also discussed its critical value in the identification of neuropsychiatric disorders and the validation of therapeutic approaches.

• National Natural Science Foundation of China

Major depressive disorder (MDD), also called depression, is a serious disease that impairs the quality of life of patients and has a high incidence, affecting approximately 3.8% of the world population. Its diagnosis is very subjective and is not supported by measurable biomarkers mainly due to the lack of biochemical markers. Recently, disturbance of lipid profiling has been recognized in MDD, in animal models of MDD or in depressed patients, which may contribute to unravel the etiology of the disease and find putative new biomarkers, for a diagnosis or for monitoring the disease and therapeutics outcomes. In this review, we provide an overview of current knowledge of lipidomics analysis, both in animal models of MDD (at the brain and plasma level) and in humans (in plasma and serum). Furthermore, studies of lipidomics analyses after antidepressant treatment in rodents (in brain, plasma, and serum), in primates (in the brain) and in humans (in plasma) were reviewed and give evidence that antidepressants seem to counteract the modification seen in lipids in MDD, giving some evidence that certain altered lipid profiles could be useful MDD biomarkers for future precision medicine.

• Animals
• Biomarkers/metabolism
• Depression/diagnosis
• Depression/drug therapy
• Depression/metabolism
• Humans
• Lipid Metabolism
• Oxidative Stress

• deep learning
• machine learning
• mental disorders
• natural language processing
• precision medicine

• DNA methylation
• Epigenomics
• Genome
• Metabolome
• Micro-RNA
• Suicide

In psychiatry, the selection of antipsychotics and antidepressants is generally led by a trial-and-error approach. The prescribing of these medications is complicated by sub-optimal efficacy and high rates of adverse drug reactions (ADRs). These both contribute to poor levels of adherence. Pharmacogenetics (PGx) considers how genetic variation can influence an individual’s response to a drug. Pharmacogenetic testing is a tool that could aid clinicians when selecting psychotropic medications, as part of a more personalized approach to prescribing. This may improve the use of and adherence to these medications. Yet to date, the implementation of PGx in mental health environments in the United Kingdom has been slow. This review aims to identify the current barriers and enablers to the implementation of PGx in psychiatry and determine how this can be applied to the uptake of PGx by NHS mental health providers. A systematic searching strategy was developed, and searches were carried out on the PsychInfo, EmBase, and PubMed databases, yielding 11 appropriate papers. Common barriers to the implementation of PGx included cost, concerns over incorporation into current workflow and a lack of knowledge about PGx; whilst frequent enablers included optimism that PGx could lead to precision medicine, reduce ADRs and become a more routine part of psychiatric clinical care. The uptake of PGx in psychiatric care settings in the NHS should consider and overcome these barriers, while looking to capitalize on the enablers identified in this review.

• mental health
• pharmacogenetic testing
• pharmacogenetics
• pharmacogenomics
• psychiatry

• Major depressive disorder
• antidepressant
• augmentation
• combination
• outcomes
• pharmacotherapy
• sequential treatment
• switch
• treatment algorithm

• Antidepressive Agents/therapeutic use
• Depression
• Depressive Disorder, Major/drug therapy
• Drug Therapy, Combination
• Humans
• Recurrence

• CYP450
• drug-drug interaction
• metabolism
• neurobiology
• neuropharmacology
• side effects

• P4 medicine
• affective disorders
• digital data collection
• psychiatry

• Android app
• gamification
• personalized medicine
• pharmacogenomics

• British Pharmacological Society Teaching Grants Years 2015 and 2018 Fondo integrativo per la ricerca (Fir)-Annualità 2018 and 2019

Treatment-resistant schizophrenia (TRS) is a common phenotype of schizophrenia that places a considerable burden on patients as well as on society. TRS is known for its tendency to relapse and uncontrollable nature, with a poor response to antipsychotics other than clozapine. Therefore, it is urgent to identify objective biological markers, so as to guide its treatment and associated clinical work. In the present study, the peripheral blood mononuclear cells (PBMCs) of patients with TRS and a healthy control group, which were gender-, age- and ethnicity-matched, were subjected to microRNA (miRNA/miR) sequencing to screen out the top three miRNAs with the highest fold change values. These were then validated in the TRS (n=34) and healthy control (n=31) groups by reverse transcription-quantitative PCR. For two of the top three miRNAs, the PCR results were in accordance with the sequencing result (P<0.01), while the third miRNA exhibited the opposite trend (P<0.01). To elucidate the functions of these two miRNAs, Homo sapiens (hsa)-miR-218-5p and hsa-miR-1262 and their regulatory network, target gene prediction was first performed using online TargetScan and Diana-micro T software. Bioinformatics analysis was then performed using functional enrichment analysis to determine the Gene Ontology terms in the category biological process and the Kyoto Encyclopedia of Genes and Genomes pathways. It was revealed that these target genes were markedly associated with the nervous system and brain function, and it was obvious that the differentially expressed miRNAs most likely participated in the pathogenesis of TRS. A receiver operating characteristic curve was generated to confirm the distinct diagnostic value of these two miRNAs. It was concluded that aberrantly expressed miRNAs in PMBCs may be implicated in the pathogenesis of TRS and may serve as specific peripheral blood-based biomarkers for the early diagnosis of TRS.

Major depressive disorder (MDD) is a common severe psychiatric illness, exhibiting sub-optimal response to existing pharmacological treatments. Although its etiopathogenesis is still not completely understood, recent findings suggest that an altered composition of the gut microbiota might play a role. Here we aimed to explore potential differences in the composition of the gut microbiota between patients with MDD and healthy controls (HC) and to identify possible signatures of treatment response by analyzing two groups of MDD patients characterized as treatment-resistant (TR) or responders (R) to antidepressants. Stool samples were collected from 34 MDD patients (8 TR, 19 R and 7 untreated) and 20 HC. Microbiota was characterized using the 16S metagenomic approach. A penalized logistic regression analysis algorithm was applied to identify bacterial populations that best discriminate the diagnostic groups. Statistically significant differences were identified for the families of Paenibacillaceae and Flavobacteriaceaea, for the genus Fenollaria, and the species Flintibacter butyricus, Christensenella timonensis, and Eisenbergiella massiliensis among others. The phyla Proteobacteria, Tenericutes and the family Peptostreptococcaceae were more abundant in TR, whereas the phylum Actinobacteria was enriched in R patients. Moreover, a number of bacteria only characterized the microbiota of TR patients, and many others were only detected in R. Our results confirm that dysbiosis is a hallmark of MDD and suggest that microbiota of TR patients significantly differs from responders to antidepressants. This finding further supports the relevance of an altered composition of the gut microbiota in the etiopathogenesis of MDD, suggesting a role in response to antidepressants.

• antidepressant resistance
• gut-brain axis
• major depressive disorder
• microbiota