Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review

IntroductionPolycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS.MethodsWe applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022.Results135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies).ConclusionArtificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022295287

KLASIFIKASI DATA MICROARRAY MENGGUNAKAN DISCRETE WAVELET TRANSFORM DAN NAIVE BAYES CLASSIFICATION

Saat ini, kanker adalah salah satu penyakit paling mematikan. Sehingga, dibutuhkan sebuah program untuk deteksi kanker secara akurat. Pada data kanker biasanya data berupa data microarray. Dimana, atribut terdiri dari informasi gen seorang individu dan data objek adalah individu-individu yang terdeteksi kanker. Informasi gen terdiri dari jumlah yang sangat banyak hingga mencapai puluhan ribu. Sedangkan, jumlah individu berdasarkan jenis kanker namun hanya berkisar puluhan hingga ratusan individu. Tugas akhir ini bertujuan untuk melakukan proses klasifikasi deteksi kanker dengan mereduksi atribut menggunakan Discrete Wavelet Transform family daubechies4 (db4) kemudian dilakukan proses klasifikasi menggunakan Naive Bayes. Lalu hasil akan dibandingkan dengan menggunakan seleksi atribut Minimum-Redundancy Maximum-Relevance jenis F-Test Correlation Difference dengan metode klasifikasi Naive Bayes. Pengujian yang dilakukan mengambil jumlah atribut terbaik pada metode db4. Sistem yang dibuat menggunakan db4 dengan metode klasifikasi Naive Bayes mendapatkan hasil yang baik. Dimana, nilai akurasi mencapai 98,4126%. **Kata Kunci:** Kanker, data microarray, daubechies4, Minimum-Redundancy Maximum-Relevance, Naive Bayes

Analisis Seleksi Fitur Genetic Algorithm Dan Ekstraksi Fitur Wavelet Pada Klasifikasi Microarray Data Menggunakan Naïve Bayes

Microarray adalah teknik modern yang memfasilitasi analisis simulasi dari sejumlah data ekspresi gen yang besar yang diperlukan untuk memecahkan masalah biologis yang kompleks. Oleh karena itu, diperlukan skema yang didalamnya terdapat proses reduksi dimensi dan proses klasifikasi. Dalam hal ini, proses reduksi dimensi bertujuan untuk meringankan beban komputasi pada klasifikasi, proses reduksi yang digunakan yaitu seleksi fitur Genetic Algorithm dan ekstraksi fitur Wavelet Haar. Kemudian, proses klasifikasi bertujuan untuk mengklasifikasikan data kanker atau bukan kanker, dengan menggunakan metode klasifikasi Naïve Bayes. Adapun akurasi terbaik dari seleksi fitur Genetic Algorithm pada data colon tumor 52,9412%, penyakit lung cancer 88,2452% dan ovarian 75%. Sedangkan, performansi terbaik dari ekstraksi fitur wavelet Haar memberikan hasil untuk penyakit colon tumor sebesar 80%, penyakit lung cancer 94,1176% dan ovarian 100%. Kata kunci : data microarray, naïve bayes, genetic algorithm, wavelet haa

Dissecting the ontogeny and functional relevance of altered GABAergic circuitry in Polycystic Ovary Syndrome (PCOS)

Polycystic ovary syndrome (PCOS) is the most common cause of female infertility worldwide, yet this prevalent endocrine disorder remains poorly understood. Although classically considered an ovarian disease, altered brain wiring may play a central role in the pathogenesis of PCOS. Neuroendocrine derangements in PCOS include elevated luteinizing hormone (LH) pulse secretion, which likely results from high gonadotropin-releasing hormone (GnRH) pulse frequency and mirrors defects within the GnRH neuronal network. Enhanced GABA actions in GnRH neurons have been proposed to be a culprit of altered GnRH/LH secretion and underlie some of the pathological features of the disorder. Pre-clinical and clinical evidence support the idea that prenatal androgen excess may program an abnormal GABA-to-GnRH neuron circuit to develop PCOS during adult life. In this regard, the present study aimed to answer fundamental questions about the ontogeny, rescue and biological function of altered GABAergic innervation to GnRH neurons. This PhD project used a prenatally androgenized (PNA) mouse model that recapitulates the cardinal features of PCOS and mimics the elevated LH pulse frequency of the disease. Initial studies aimed to address whether GnRH neuronal network changes observed in PNA mice were programmed through early androgen exposure or driven by adult androgen excess. Control and PNA GnRH-GFP transgenic mice were evaluated at postnatal day (PND) 25, prior to pubertal onset. Confocal imaging and analysis of GABA inputs onto GnRH neurons revealed that GABAergic contact was significantly increased in prepubertal PNA mice (P < 0.05). In addition, circulating testosterone levels at PND 25 were not different between PNA and control groups, suggesting that brain circuit abnormalities were not dependent upon early manifestation of androgen excess in a PCOS-like condition. Serial blood sampling defined the developmental timing of androgen excess in PNA mice, showing that circulating testosterone levels rise significantly during early adulthood (PND 50 and PND 60) in PNA animals when compared to controls (P < 0.01). All hyperandrogenic PNA mice presented disruption of estrous cyclicity, displaying significant arrest in the metestrous stage and a complete absence of the proestrous stage (P < 0.0001), indicating anovulatory cycles. To determine whether neuroendocrine derangements of LH regulation would persist after the removal of hyperandrogenic ovaries, the same cohort of mice were ovariectomized (OVX) and serial blood sampling was performed to investigate LH pulse dynamics. Although LH pulse frequency was similar between the groups, OVX PNA mice exhibited greater LH pulse amplitude (P < 0.0001) and magnitude of LH release than controls (P < 0.001), implying that defects within the GnRH network remained in the absence of hyperandrogenic ovaries and suggesting that the primary pathology of PCOS is in the brain. Compelling clinical evidence indicates that long-term androgen receptor (AR) blockade with flutamide (Flut) is able to restore both the sensitivity of the GnRH pulse generator and menstrual cyclicity in PCOS women. This PhD project tested the hypothesis that these improvements may be the result from plastic changes in the brain that rescue normal GABAergic wiring to GnRH neurons. Control and PNA mice were treated with Flut (25 mg/kg/day) or an oil vehicle from PND 40 to PND 60. GABA inputs to GnRH neurons were assessed as previously performed for prepubertal animals and confirmed that oil-treated adult PNA mice display enhanced GABAergic contact on GnRH neurons (P < 0.01). Remarkably, Flut treatment was able to decrease and rescue normal GABA-to-GnRH neuron circuit features in PNA mice (F1, 21 treatment = 41.8; P < 0.0001). Results also showed that estrous cyclicity of PNA mice improved considerably during Flut treatment. Evaluation of ovarian morphology treatment showed that AR signaling blockade improved preovulatory follicle recruitment and restored normal features of the granulosa and theca cell layers in these follicles. Previous neuroanatomical work indicates that increased GABAergic wiring to GnRH neurons in PNA mice originates largely from the arcuate nucleus (ARN) of the hypothalamus. In this PhD project, I investigated the functional role of GABA neurons originating in the ARN in regulating LH secretion using in vivo optogenetics. Selective targeting and expression of channelrhodopsin-2 E123T accelerated variant (ChETA) in the ARN GABA neurons was achieved using vesicular GABA transporter (VGAT)-Cre mice. ARN GABA neurons were activated by delivering blue light pulses (5ms) at 2 and 20 Hz during 10 minutes in diestrus female, male and PNA mice. Optogenetic activation at 20 Hz elicited robust LH release similarly in male and diestrus female (P < 0.05), whereas 2-Hz stimulation failed to evoke changes in LH levels. Interestingly, 20-Hz light stimulation in PNA mice induced smaller changes in LH levels when compared to male and diestrus female groups (P < 0.05). These data suggest that altered LH release in PNA mice might reflect a decreased pituitary LH releasable pool due to a formerly high GnRH pulse frequency stimulation in the PCOS-like condition. Together, these findings support the idea that a prenatal androgen insult can program altered GABAergic brain circuits early in development, prior to pubertal onset, and might be the culprit for developing subsequent androgen excess during early adulthood. This PhD thesis also highlights that abnormal GABA-to-GnRH neuron circuit remains plastic in adult PNA mice; and that the specific GABAergic pathway from ARN GABA neuron is biologically relevant to modulate LH secretion. These data support the important role of ARN GABA neurons in the regulation of the GnRH neuron biology in healthy fertility and in the pathophysiology of PCOS

Cultivate Quantitative Magnetic Resonance Imaging Methods to Measure Markers of Health and Translate to Large Scale Cohort Studies

Magnetic Resonance Imaging (MRI) is an indispensable tool in healthcare and research, with a growing demand for its services. The appeal of MRI stems from its non-ionizing radiation nature, ability to generate high-resolution images of internal organs and structures without invasive procedures, and capacity to provide quantitative assessments of tissue properties such as ectopic fat, body composition, and organ volume. All without long term side effects. Nine published papers are submitted which show the cultivation of quantitative measures of ectopic fat within the liver and pancreas using MRI, and the process of validating whole-body composition and organ volume measurements. All these techniques have been translated into large-scale studies to improve health measurements in large population cohorts. Translating this work into large-scale studies, including the use of artificial intelligence, is included. Additionally, an evaluation accompanies these published studies, appraising the evolution of these quantitative MRI techniques from the conception to their application in large cohort studies. Finally, this appraisal provides a summary of future work on crowdsourcing of ground truth training data to facilitate its use in wider applications of artificial intelligence.In conclusion, this body of work presents a portfolio of evidence to fulfil the requirements of a PhD by published works at the University of Salford

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Policy Implementation Analysis of District Health System to Improve Health Services: Study in North Central Timor Regency, East Nusa Tenggara Timur Province, Indonesis

Context: Improving degree of public health in a region requires quality health services. For this reason, district health system has been formed which can be implemented comprehensively to the target community. A study is needed to find out the factors that influence policy implementation so that quality of health services can be improved. This study used quantitative method with structural equation models to find patterns of the relationship between the district health system and health services. The results showed that there are 7 indicators that are part of the district health system factors, 2 indicators that are part of the resposivensss factor, 8 indicators that are part of the policy implementation factor, and 3 indicators that are part of the health service factor. These indicators have loading factor ≥ 0.5. The district health system consisting of 7 subsystems if properly implemented will have a positive impact on health services by 1.98. Contribution of policy implementation in improving health services will be great if the district health system is implemented together with responsiveness, so that the total effect becomes 2.20

Synthesis of new pyrazolium based tunable aryl alkyl ionic liquids and their use in removal of methylene blue from aqueous solution

In this study, two new pyrazolium based tunable aryl alkyl ionic liquids, 2-ethyl-1-(4-methylphenyl)-3,5- dimethylpyrazolium tetrafluoroborate (3a) and 1-(4-methylphenyl)-2-pentyl-3,5-dimethylpyrazolium tetrafluoroborate (3b), were synthesized via three-step reaction and characterized. The removal of methylene blue (MB) from aqueous solution has been investigated using the synthesized salts as an extractant and methylene chloride as a solvent. The obtained results show that MB was extracted from aqueous solution with high extraction efficiency up to 87 % at room temperature at the natural pH of MB solution. The influence of the alkyl chain length on the properties of the salts and their extraction efficiency of MB was investigated