A REVIEW ON AYURVEDIC MANAGEMENT OF POSTMENOPAUSAL SYNDROME (RAJONIVRITTI LAKSHANAS)

Rajonivruttijanya Lakshana is a group of symptoms produced by degenerative changes. It requires a major healthcare initiative to improve the quality of life of women after Menopause. Post-Menopausal Symptoms are Hot flushes, Fatigue, Disturbed Sleep, Dyspareunia, Vaginal dryness, Leucorrhoea, Decrease Sexual desire, De- pression, Skin wrinkling, Anxiety, Mood swing, dementia, inability to concentrate, Osteoporosis etc. Ayurvedic treatment of Menopause focuses on strengthening and rejuvenating the reproductive system and whole body. Management of Rajonivruttijanya Lakshana through Rasayan Chikitsa, Abhangya, Basti, Shirodhara supplement of Phytoestogens, Bruhaniya, Balya and Vayasthapan drugs along with maintenance of mental health with the help of Yoga, Asanas, meditation and with Ahara and Vihara helps menopausal females to change annoying men-opause to healthy and happy menopause. Avoid the provocative causes of Vata dosha as there is natural vitiation of vata dosha with advancing age. Keywords: Rajonivritti Lakshanas, Postmenopausal Syndrome, Panchakarma, Ayurvedic medicinal plants.

Psychological Impact of Covid-19 Issues Appearing on Social Media

The effect of disseminating content on social media on people's psychology is discussed in this essay. The position of social media in disseminating content with the aim of informing and supplying the public with the most up-to-date information on covid-19 problems is critical. Over the pandemic, mental wellbeing conditions have worsened as a result of the widespread dissemination of inaccurate knowledge about Covid matters, causing a slew of psychiatric challenges in community. Learning difficulties, mood swing symptoms, somatic complaints, and unnecessary fear triggered by the spread of associated facts covid-19, which seems to be quite frightening and risky. The widespread dissemination of hoaxes about Covid-19 on social media has caused widespread paranoia and terror, leading to panic shopping, which has culminated in skyrocketing costs for essential necessities due to shortages, to the point that surgical masks and hand sanitizers are no longer accessible. It not only causes erroneous beliefs, but it also causes uncertainty and anxiety, as well as affecting people's mental health.

Gambaran Premenstrual Syndrome pada Remaja Periode Akhir di Fakultas Kedokteran Universitas Sam Ratulangi

Premenstrual syndrome (PMS) is the most common untreated disorder and a public health problem among women of reproductive age, which adversely affects mental well-being, quality of life, and academic achievement. This study was aimed to determine the premenstrual syndrome signs and symptoms most experienced by late adolescents. This was a descriptive and survey study using questionnaire distributed through google form to 142 female students from first and third semester of Faculty of Medicine Sam Ratulangi University academic year 2020/2021. The results showed that the most common type of PMS symptoms was psychological symptoms found in 136 respondents (95.8%); the most common behavioral symptom was fatigue in 93 respondents (65.5%), the most common physical symptom was acne in 122 respondents (85.9%); and the most psychological symptom was mood swing in 125 respondents (88%). In conclusion, the most common premenstrual syndrome symptom found in late adolescent at the Faculty of Medicine Sam Ratulangi University according to the type of symptom was psychological symptom.Keywords: premenstrual syndrome, adolescent Abstrak: Premenstrual syndrome (PMS) merupakan kelainan umum yang tidak diobati dan masalah kesehatan masyarakat di kalangan wanita usia reproduksi, yang berdampak buruk pada kesejahteraan mental, kualitas hidup dan prestasi akademik. Penelitian ini bertujuan untuk mengetahui tanda dan gejala PMS yang paling banyak dialami remaja periode akhir. Penelitian ini menggunakan metode survei deskriptif dengan alat kuesioner yang dibagikan melalui google form pada 142 mahasiswi semester 1 dan 3 Fakultas Kedokteran Universitas Sam Ratulangi tahun ajaran 2020/2021. Hasil penelitian mendapatkan jenis gejala PMS yang paling banyak dialami ialah gejala psikologis pada 136 responden (95,8%), gejala perilaku yang paling banyak dialami ialah kelelahan pada 93 responden (65,5%), gejala fisik yang paling banyak dialami ialah muncul jerawat pada 122 responden (85,9), dan gejala psikologis yang paling banyak dialami ialah mood swing pada 125 responden (88%). Simpulan penelitian ini ialah gambaran PMS pada remaja periode akhir di Fakultas Kedokteran Universitas Sam Ratulangi yang paling banyak dialami menurut jenis gejala ialah gejala psikologis.Kata kunci: premenstrual syndrome, remaja

PEMODELAN SAW UNTUK MENENTUKAN TINGKAT DEPRESI GAMER

Perkembangan teknologi saat ini melahirkan banyak permainan berbasis gadget dan internet yang menarik. Seerorang dapat bermain game untuk menghilangkan kepenatan dan memperoleh perasaan bahagia. Namun, disisi lain seorang gamer juga dapat mengalami depresi karena faktor-faktor lingkungan yang mempengaruhi. Tingkat depresi seorang Gamer dapat diukur dari beberapa kriteria yaitu Insomsia, Mood Swing, Tidak nafsu makan, kesulitan komunikasi dan anti sosial. Beberapa kriteria tersebut dimodelkan menggunakan metode Simple Additive Weighting (SAW) yang merupakan salah satu metode untuk pemilihan keputusan. Pemodelannya digunakan untuk menentukan tingkat depresi pada gamer dengan hasil yang didapat terdapat 3 tingkatan depresi mulai dari ringan sampai tinggi. Dengan hasil terbanyak berada pada tingkat sedang lalu banyak dan terakhir dan ringan. Tujuan dilakukan pemodelan ini adalah memberikan tambahan pengetahuan tentang tingkatan depresi gamer. Bahwa seorang gamer dapat mengalami depresi walaupun kegiatan bermain game seharusnya menjadi sesuatu yang menyenangkan.

Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?

Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the ‘moment-by-moment quantification of the individual-level human phenotype in its own environment’, represents a new approach aimed at measuring the human behavior and may theoretically enhance clinicians’ capability in early identification, diagnosis, and management of any mental health conditions, including BD. Moreover, a digital phenotyping approach may easily introduce and allow clinicians to perform a more personalized and patient-tailored diagnostic and therapeutic approach, in line with the framework of precision psychiatry. The aim of the present paper is to investigate the role of digital phenotyping in BD. Despite scarce literature published so far, extremely heterogeneous methodological strategies, and limitations, digital phenotyping may represent a grounding research and clinical field in BD, by owning the potentialities to quickly identify, diagnose, longitudinally monitor, and evaluating clinical response and remission to psychotropic drugs. Finally, digital phenotyping might potentially constitute a possible predictive marker for mood disorders.

Tracking and Monitoring Mood Stability of Patients with Major Depressive Disorder by Machine Learning Models Using Passive Digital Data (Preprint)

BACKGROUND Major Depressive Disorder(MDD) is a common mental illness characterized by persistent sadness and a loss of interest in activities. Using smartphones and wearable devices to monitor MDD patients’ mental condition has been examined in several studies. However, there are few studies utilizing passively collected data to monitor mood changes in a time period. OBJECTIVE We aimed to examine the feasibility of monitoring mood status and stability of MDD patients using machine learning (ML) models trained by passively collected data including phone usage data, sleep data and step count data. METHODS We constructed 612 data samples representing time spans during 3 consecutive Patient Health Questionnaire-9(PHQ-9) assessments. Each data sample was labeled as Steady or Mood Swing with subgroups Steady-remission, Steady-depressed, Mood Swing-drastic, Mood Swing-moderate based on patients’ PHQ-9 scores from 3 visits. 252 features were extracted, and 4 feature selection models were applied. 6 different combinations of types of data were experimented using 6 different machine learning models. RESULTS A total of 334 participants with MDD were enrolled in this study. The highest accuracy of classification between Steady and Mood Swing was 76.62% and recall was 91.53% with features from all types of data being used. Among 6 combinations of types of data we experimented, the overall best combination was using Call Logs, Sleep data, Step count data and Heart rate data. The accuracies of predicting between Steady-remission and Mood Swing-drastic, Steady-remission and Mood Swing-moderate, Steady-depressed and Mood Swing-drastic were over 80%, and the accuracy of predicting between Steady-depressed and Mood Swing-moderate and the overall Steady to Mood Swing classification accuracy were over 75%. Comparing all 6 combinations above, we found that the overall prediction accuracies between Steady-remission and Mood Swing (drastic and moderate), are better than those between Steady-depressed and Mood Swing (drastic and moderate). CONCLUSIONS Our proposed method could be used to monitor MDD patients’ mood changes with a promising accuracy utilizing passively collected data, which can be used as a reference to doctors for adjusting treatment plans or a warning of relapse to patients and their guardians. CLINICALTRIAL Chinese Clinical Trial Registry (ChiCTR)(www.chictr.org.cn):ChiCTR1900021461

Hero: Automated Detection System for Prescription Stimulant Overdose via Synthesized GNP-based Metabolite Detection, AI-based Emotion Inference, and Novel Biometric Measurement

Over the past year, approximately 10,000 American teens have died due to a prescription stimulant overdose. A comprehensive, accurate, and easily integrable approach to detect a drug overdose in the environment where it occurs is imperative. This study proposes a multi-factor automated overdose detection system designed to operate in a teen’s living and working space. The eight factors include sweat composition analysis, mood swing detection, measurement of vital signs (heart rate, blood pressure, respiration rate, electrodermal activity), detection of spasms, and consciousness verification. The two-factor reagent strip, consisting of a gold nanoparticle-based diagnostic measure and pH-based control measure, displayed a significant difference in color between simulated case and control sweat. An Arduino-based sensor and hardware apparatus were built and evaluated to integrate the strip onto a laptop mouse. A generalized linear model, gradient boosting machine, and multilayer perceptron trained on over 1.6 million data points were successfully able to detect a mood swing from a baseline emotion. The final model (with an AUC of over 85%) was implemented into the Hero mobile app to monitor a teen’s outgoing SMS messages for a mood swing. A mathematical image processing algorithm to measure vital signs was created, and repeated evaluation resulted in a percent error of <5%. These results, along with external sensors/hardware devices and mobile app features, were incorporated into Hero. In contrast to previous solutions, teens can use the Hero system and mobile app for constant background monitoring of physical, emotional, and biochemical signs of overdose in their daily life.

The Construction of the Psycho-sociological Knowledge on Diabetes Mellitus

This study aims to analyze the social representations elaborated by diabetic’s people on diabetes mellitus and your treatment. The sample was composed of 80 participants with ages between 21 and 83 years old (M= 55.92, SD = 12.06), who answered to a sociodemographic questionnaire and the Word Association Test. The data were submitted to SPSS - 19.0 and Tri-deux-mots software and analyzed using descriptive statistics and factorial correspondence analysis. The results showed that the psychosocial knowledge construction of diabetes doesn´t differ from scientific knowledge, while chronic disease, related to blood sugar problems, mood swing and life danger. The treatment, emerged associated as a food control, adherence and follow medical guidelines. It is hoped, that these results will contribute to enlarge the this disease visibility and with the therapeutic practices development in the context of diabetes, targeting both the physical and emotional aspects.