Publication Search

Hypertension is one of the most common chronic diseases among the elderly and is often accompanied by insomnia. This condition can increase anxiety and reduce quality of life. Progressive Muscle Relaxation (PMR) is an effective non-pharmacological therapy to lower blood pressure and improve sleep quality. This paper describes nursing care for elderly patients with hypertension and insomnia through PMR application at RSUD Embung Fatimah Batam City in 2025. A case study approach was used through the nursing process, including assessment, nursing diagnosis, intervention, implementation, and evaluation. The main nursing diagnosis was sleep pattern disturbance related to physical discomfort and anxiety. Interventions included education and PMR exercises for six consecutive days. Results showed blood pressure decreased from 180/100 mmHg to 130/80 mmHg, sleep quality improved from difficulty sleeping to 6–7 hours per night, and anxiety levels reduced. Progressive muscle relaxation therapy proved effective as an independent nursing intervention to reduce blood pressure and improve sleep quality in elderly patients with hypertension and insomnia. Nurses should use this therapy as a routine non-pharmacological intervention in gerontological nursing practice.

Ventricular septal defect (VSD) is a congenital heart defect characterized by a gap in the interventricular septum, with a prevalence of 20–30% of all cases of congenital heart disease. Based on their location, VSD is divided into perimembrane, muscular, and subarterial. This case report discusses a patient who came to the emergency room of Cut Meutia Hospital with good consciousness (GCS E4M6V5) and complained of shortness of breath, cough, nausea, and chest pain spreading to the neck and hands. The anamnesis showed chronic symptoms in the form of coughing up phlegm for two weeks, accompanied by throbbing headaches, dizziness, flatulence, and sleep disturbances. Physical examination showed vital signs of TD 140/90 mmHg, HR 86 x/min, RR 23 x/min, SpO₂ 37%, and temperature 37°C.  Supporting examinations in the form of chest X-rays showed cardiomegaly of the pulmonary segment, while ECG showed rhythmic sinuses with LAD axis, T-inverted in AVL, V1, V2, RsR' in V2, positive Cornell criteria, anterolateral myocardial infarction, RBBB, and LVH. Echocardiography shows the presence of a bidirectional shunt with a dominant right-to-left shunt. This case confirms the importance of comprehensive diagnosis through clinical and supporting examinations to determine optimal management in patients with VSD, as well as prevent further complications.

Individual behavioral factors are critical determinants of organizational effectiveness and a vital component of modern organizational diagnosis. This study aims to identify and map individual behavioral factors as strategic indicators in organizational diagnosis using a Systematic Literature Review (SLR) approach guided by PRISMA 2020. Analysis of ten selected articles reveals that organizational effectiveness in the digital transformation era is driven by a reciprocal equilibrium between an individual's cognitive infrastructure and volatile work environment demands. The findings synthesize these behaviors into four strategic clusters: psychological well-being as primary infrastructure, digital structural support audits, justice and trust equilibrium, and psychological contract synchronization. This research provides tactical implications for Human Capital practitioners to transform annual diagnostic methodologies toward the implementation of monthly pulse surveys to detect fluctuations in well-being and disengagement intentions in real-time. This predictive diagnostic step is essential to mitigate turnover risks and design precise institutional interventions in hybrid work ecosystems.

Vertigo is a clinical symptom that is often found and can be related to vestibular disorders or systemic conditions such as hypertension. The case report described a 58-year-old female patient who came in with complaints of dizziness that disappeared from 12 hours before admission to the hospital, accompanied by double vision, nausea, severe headache, flatulence, and stiffness of the left shoulder. Physical examination showed increased blood pressure and bidirectional nystagmus on neurological examination. Laboratory tests show mild electrolyte abnormalities and increased cholesterol. The patient was diagnosed with uncontrolled vestibular vertigo et causa hypertension and received intravenous fluid therapy, symptomatic therapy, antihypertensive therapy, and vestibular suppressants. Clinical improvements were obtained during follow-up. Education related to blood pressure control, medication adherence, and lifestyle changes such as low-salt diet settings and stress management is also provided to patients. In addition, periodic monitoring is required to prevent recurrence and further complications. This case emphasizes the importance of identifying hypertension as a contributing factor to vertigo as well as comprehensive management to improve patient outcomes and improve the quality of life of patients.

Inguinal hernia is a common abdominal hernia occurring in the groin region and generally requires surgical management, with postoperative pain control being an important aspect of patient recovery. This case reports a 55-year-old male presenting with a left groin mass for 6 months, which was initially reducible but subsequently became difficult to reduce, accompanied by severe pain and activity limitation. Physical examination revealed a left inguinal mass measuring approximately 3.03 × 2.17 cm, with firm consistency, well-defined borders, immobility, and tenderness, while ultrasonography demonstrated dilation of the left inguinal canal after provocation; based on these findings, a diagnosis of left inguinal hernia with ASA physical status II was established. The patient was managed with herniorrhaphy under regional anesthesia using a subarachnoid block, followed by ultrasound-guided ilioinguinal and iliohypogastric nerve blocks with levobupivacaine and pehacain for postoperative analgesia. Intraoperatively, the patient remained hemodynamically stable, and postoperatively, pain intensity decreased gradually from a VAS score of 6 to 2 within 24 hours and reached 0 on the second postoperative day without significant complaints. This case demonstrates that regional anesthesia combined with ultrasound-guided peripheral nerve block may support postoperative pain control after herniorrhaphy, maintain clinical stability, and improve patient comfort during recovery.

Atrophic rhinitis or ozena is a chronic infection of the nasal cavity characterized by progressive atrophy of the nasal mucosa and turbinate bones accompanied by the formation of foul-smelling nasal crusts. This condition is more commonly found in females, particularly during puberty, and is frequently associated with populations of low socioeconomic status and poor environmental sanitation. The exact etiology of atrophic rhinitis remains unclear; however, several factors are believed to contribute, including chronic infection by *Klebsiella ozaenae*, vitamin A and iron deficiency, chronic sinusitis, hormonal imbalance, collagen diseases, trauma, and genetic predisposition. Common clinical manifestations include nasal obstruction, thick greenish nasal discharge, crust formation, epistaxis, anosmia, and *foetor ex nasi*, which is often more noticeable to people around the patient. Anterior rhinoscopy typically reveals widened nasal cavities, turbinate atrophy, thin and dry nasal mucosa, as well as purulent secretions and greenish crusts. Diagnosis is established through history taking, ENT physical examination, and supporting investigations such as microbiological culture, histopathological examination, radiological imaging, and paranasal sinus CT-scan. Management may be conservative with antibiotics, nasal irrigation, nasal drops, vitamin A supplementation, and iron preparations, or surgical in severe cases or those unresponsive to conservative therapy.

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.

Bronchopneumonia is a type of infection of the lower respiratory tract that often occurs in children, especially toddlers. This disease is characterized by inflammation of the bronchioles and lung tissue which triggers increased production of secretions in the airways. The buildup of secretions often causes nursing problems in the form of ineffective airway clearance, which is indicated by ineffective coughing, rapid breathing, shortness of breath, and the appearance of additional breath sounds such as rales. Chest physiotherapy in children was carried out to evaluate the effectiveness of the clapping technique in helping to clear the airway in bronchopneumonia patients. The method applied in evidence-based practice (EBP) uses case studies with a nursing care approach. Intervention was given to three patients with a diagnosis of bronchopneumonia for three days, with a duration of approximately 1-2 minutes in each area of ​​the chest. Evaluation is carried out using a stethoscope and measuring time (clock), with the results recorded on an observation sheet. After chest physiotherapy (clapping), the three patients showed improvement in airway clearance, which was indicated by respiratory frequency returning to normal, no additional breath sounds (ronchi), and reduced secretion production. Based on these results, it can be concluded that the application of EBP through chest physiotherapy is effective in increasing airway clearance in bronchopneumonia patients.

Students in the transition period are vulnerable to experiencing mental health problems and tend to self-diagnose due to easy access to information and low mental health literacy. This research aims to determine the relationship between mental health literacy and the tendency to make a diagnosis in students at the Faculty of Public Health, Nusa Cendana University, Kupang. This type of research is quantitative research with a correlational approach. Respondents in this study numbered 301 people using probability sampling techniques. Data collection techniques used the mental health literacy scale and self-diagnosis scale. Data analysis uses the product moment correlation test. The research results show that there is a significant negative relationship between mental health literacy and self-diagnosis (r = -0.846 < 0.05), which means that the higher the mental health literacy, the lower the tendency for self-diagnosis in students, and vice versa. In addition, mental health literacy contributed 71.6% to the tendency of self-diagnosis. It is hoped that students not only have a high level of mental health literacy but also quality, so that they realize that self-diagnosis is an inappropriate action and students are expected to take advantage of available professional mental health services.

Leprosy remains a significant public health problem in various developing countries, including Indonesia. This disease is caused by Mycobacterium leprae, which attacks the skin and peripheral nerves and can cause long-term physical, psychological, and social impacts if not treated early and adequately. Health-seeking behavior in leprosy patients is also influenced by structural factors such as the availability of health facilities, distance, transportation costs, and the quality of communication between health workers and patients. Research in various endemic countries confirms that friendly, accessible primary health care services that provide comprehensive education can improve early detection and therapy adherence. Patient SA, an 11-year-old child diagnosed with multibacillary (MB) leprosy and has been undergoing treatment for four months. The progression of the disease to MB indicates limitations in early symptom recognition and the family's health-seeking behavior, which may be influenced by low perceptions of disease susceptibility and severity, lack of knowledge, and barriers to accessing health services. However, after the diagnosis was confirmed and education provided by health workers, positive behavioral changes occurred, indicated by the patient's adherence to routine therapy as recommended.

Background: Autoimmune Hemolytic Anemia (AIHA) is a rare immunological condition that causes the destruction of red blood cells through autoantibodies. Case Presentation: This report discusses the case of a 38-year-old woman who presented with complaints of weakness and acute dizziness at RedaBolo, with a history of recurrent anemia over the past six months. Physical examination revealed clear signs of hemolysis, including icteric sclera and splenomegaly. Laboratory findings confirmed severe anemia (Hb 5.0 g/dL) with varied peripheral blood cell morphology (spherocytes, schistocytes, and teardrop cells). The diagnosis was supported by a positive Coombs test and hyperbilirubinemia.  Management: The therapeutic strategy focused on hemodynamic stabilization through transfusion of 4 units of packed red blood cells (PRC), immunosuppression with methylprednisolone 125 mg/day, and gastric protection. Conclusion: These interventions yielded satisfactory results, with the patient achieving significant clinical improvement and a hemoglobin level of 9.5 g/dL by the fourth day of treatment.

Background: Trigeminal neuralgia (TN) is often associated with neurovascular compression in the trigeminal nerve root entry zone, necessitating the simultaneous visualisation of nerves and blood vessels. Fusion of 3D SPACE and 3D TOF MRA images provides an integrated neurovascular view; however, not all hospitals have fusion software. This study developed a MATLAB-based image fusion method as an alternative and evaluated its equivalence to hospital-based fusion software.Methods: This study employed a descriptive quantitative research design, conducted in November 2025 at Diponegoro National Hospital and Dr Kariadi General Hospital in Semarang. A total of 16 brain MRI datasets (3D SPACE and 3D TOF MRA) were fused using hospital software and the MATLAB fusion application (MATLAB R2025b GUI). The fusion results were assessed by specialist radiologists. Diagnostic performance metrics (sensitivity, specificity, NDP, NDN, accuracy) were calculated, and paired differences were tested using the McNemar test. Intra-observer reliability was assessed using percentage agreement and Cohen’s Kappa.Results: MATLAB fusion yielded a sensitivity of 90.91%, specificity of 80.00%, NDP of 90.91%, NDN of 80.00%, and accuracy of 87.50%; the McNemar test (p=1.000) indicated no significant difference. Intra-observer reliability was very good (percent agreement 94%; Kappa 0.875). These findings indicate that MATLAB-based fusion is equivalent to hospital software fusion on the study data and has the potential to serve as an alternative in facilities without fusion software, provided that registration standardisation and user training are in place.

Diabetic ketoacidosis (DKA) is a life-threatening acute metabolic complication of diabetes mellitus characterized by hyperglycemia, metabolic acidosis, and ketosis due to absolute or relative insulin deficiency. DKA may present as the initial manifestation of previously undiagnosed diabetes, particularly in young adults. We report a case of a 27-year-old male who presented with decreased consciousness preceded by shortness of breath. The patient had a three-month history of polyuria, polydipsia, polyphagia, and significant weight loss. Physical examination revealed a Glasgow Coma Scale score of 9 and signs of dehydration. Laboratory findings showed severe hyperglycemia (426 mg/dL), elevated HbA1c (8.5%), leukocytosis, and severe metabolic acidosis (pH 6.8; HCO₃⁻ 5.8 mmol/L). The diagnosis of DKA was established based on clinical and laboratory criteria. Management included aggressive fluid resuscitation, continuous intravenous insulin infusion, electrolyte correction, and antibiotic therapy for suspected infection. The patient showed gradual clinical improvement. This case highlights the importance of early recognition, identification of precipitating factors, and prompt management to reduce morbidity and mortality associated with DKA.

Gastric disease is a disease that cannot be taken lightly, because if it is allowed to continue it can cause other diseases to appear and can also cause death if not treated immediately. Stomach disease can be caused by an inappropriate diet, a burden on the mind and also an infection caused by bacteria. Given these problems, this study can only provide a diagnosis of gastric disease, namely gastritis, stomach ulcers, GERD, gastric cancer. The symptoms given to the system are based on common symptoms. This study aims to create an expert system for diagnosing gastric disease using the Case Based Relational (CBR) method. Based on the testing and analysis of an expert system for diagnosing gastric disease using a Web-based Case Based Reasoning (CBR), it is capable of diagnosing 5 gastric diseases Gastritis, gastric ulcer, GERD, gastric cancer by calculating the similarity value with the Case Based Reationing (CBR) method.

Pendahuluan: Kehilangan gigi dapat dikelompokan menjadi 2 jenis, yaitu kehilangan gigi sebagian ataupun seluruhnya. Kehilangan gigi sebagian atau partial edentulous digambarkan sebagai suatu kondisi hilangnya satu atau beberapa gigi, namun tidak hilang secara keseluruhan. Dalam dunia kedokteran gigi salah satu cara untuk mengatasi masalah partial edentulous yaitu dengan melakukan perawatan pengantian gigi menggunakan gigi tiruan sebagian lepasan (GTSL). GTSL merupakan solusi prostetik yang paling umum dipilih karena terbuat dari berbagai jenis bahan, seperti resin akrilik, kerangka logam dan termoplastik. GTSL resin akrilik menjadi pilihan yang paling disukai, karena sifatnya yang ekonomis, mudah dibentuk dan ringan. Laporan kasus ini menjelaskan tatalaksana kasus edentulous partial dengan pembuatan GTSL resin akrilik. Laporan Kasus: Pasien laki-laki berusia 56 tahun datang dengan keluhan adanya beberapa gigi atas dan bawah yang. Pasien menyatakan bahwa kondisi ini membuat pasien menjadi tidak nyaman saat menguyah makanan. Dari seluruh hasil pemeriksaan dapat diketahui bahwa diagnosis kasus pasien yaitu Kennedy kelas I modifikasi I dan Applegate Kennedy kelas I modifikasi 1P dengan rencana perawatan yaitu pembuatan GTSL akrilik. Tatalaksana: Tatalaksana kasus partial edentulous dengan menggunakan gigi tiruan sebagian lepasan (GTSL) akrilik ini dilakukan atas persetujuan pasien dengan rencana perawatan yang telah dijelaskan sebelumnya saat kunjungan pertama. Kesimpulan: Tatalaksana partial edentulous dengan menggunakan gigi tiruan sebagian lepasan (GTSL) berbahan resin akrilik dalam kasus ini mampu mengembalikan fungsi pengunyahan, fungsi bicara dan estetika pasien. Hasil pemeriksaan saat kontrol pasca penggunaan GTSL 1 minggu, diketahui bahwa gigi tiruan sangat nyaman selama digunakan dan tidak ditemukan adanya traumatik pada mukosa rongga mulut.

The postpartum period is a crucial phase for mothers to recover after childbirth. One of the common problems is perineal wounds, caused either by episiotomy or spontaneous rupture, which require proper care to prevent infection and accelerate healing. This case study aimed to provide midwifery care for Mrs. M, 28 years old, P3A0H3, with a second-degree perineal wound at PMB Bd. Silvi Ayu, S.Keb. The SOAP approach was applied through assessment, diagnosis, intervention, implementation, and evaluation over five days (June 12–16, 2025). Interventions included education on perineal hygiene, encouragement of nutritious food intake, light mobilization, and perineal wound care using boiled binahong leaves. The results showed decreased pain, reduced edema, a dry wound, and complete healing on the fifth day, with the REEDA score decreasing from 11 to 0. The discussion emphasized that flavonoids, saponins, and ascorbic acid in binahong leaves contribute to tissue regeneration and faster healing. Limitations of this study included the short monitoring period, limited sample, and reliance on maternal compliance. In conclusion, binahong leaf decoction was found to be effective, practical, and able to enhance maternal independence in wound care. This study is expected to serve as a reference for midwifery practice and as a basis for further research with a wider scope.

Background: Malaria transmission in Indonesia exhibits substantial spatial and temporal heterogeneity, particularly between stable endemic areas and remote outbreak-prone areas. Although routine surveillance is crucial for malaria control and elimination efforts, its limitations can obscure early warning signals, particularly in geographically isolated areas. Objective: This study aimed to analyze malaria epidemiology by integrating routine surveillance data from endemic primary health care settings with results from high-mortality outbreak investigations in remote highland districts in Indonesia, focusing on temporal trends, spatial clustering, Plasmodium species patterns, diagnostic gaps, and mortality. Methods: A mixed epidemiology approach was used. A retrospective longitudinal analysis of routine malaria surveillance data was conducted for the period 2023–2025 in endemic settings, while a cross-sectoral outbreak investigation was conducted in remote highland districts. Descriptive analyses were conducted to assess trends, demographic characteristics, species distribution, spatial heterogeneity, case detection methods, and outbreak-related mortality. Results: Routine surveillance data revealed fluctuations in malaria transmission, with a significant decline in cases in 2024 followed by a sharp increase in 2025. Conversely, outbreak investigations documented high case fatality rates, particularly affecting children and older adults, caused by delayed diagnosis, limited diagnostic capacity, and limited access to timely treatment. Conclusions: These results highlight the dynamic and context-dependent nature of malaria epidemiology in Indonesia. Integrating routine surveillance with outbreak investigations provides a comprehensive understanding of endemic trends and systemic vulnerabilities. Strengthening adaptive surveillance, improving diagnostic capacity, and implementing spatially targeted interventions are crucial for preventing outbreaks and reducing malaria-related mortality, particularly in remote and high-risk areas.

Malaria remains a major public health challenge in eastern Indonesia, particularly in the remote highland areas Papua, where access to health services is very limited. In early 2008, a suspected malaria outbreak with several deaths was reported in Agisiga District, a remote highland area in Papua. This study aimed to investigate the epidemiological characteristics, diagnostic results, and mortality patterns of the malaria outbreak as a basis for formulating future control strategies. The outbreak investigation was conducted using a cross-sectional design in Agisiga District from January to March 2008. Capillary blood samples were examined using Rapid Diagnostic Tests (RDTs) and thin blood smear microscopy. Malaria-related deaths were traced through verbal autopsy methods and interviews with families and community leaders. Descriptive analysis was performed to describe the demographic, clinical, and parasitological characteristics the cases. A total of 92 suspected malaria cases were identified, with the highest proportion coming from Tomosiga Village (52.2%), followed by Bigasiga Village (40.2%) and Agisiga Village (7.6%). Women accounted for 58.7% of all cases, and the most affected age group was 15–35 years (34.8%). Microscopic examination confirmed malaria infection in 55.0% of samples examined, with Plasmodium vivax predominating (45.0%), while the RDT positivity rate was much lower. The malaria outbreak in Agisiga District was characterized by high mortality, delayed diagnosis, and significant gaps in diagnostic capacity, particularly in remote villages. Strengthening the surveillance system, increasing diagnostic capacity, and improving access to timely treatment are crucial steps prevent similar outbreaks and reduce malaria mortality in the Papuan highlands.

Childhood tuberculosis remains a significant public health challenge, particularly in developing countries where malnutrition and limited diagnostic resources increase disease severity and mortality. Children are at high risk of rapid progression from latent infection to active disease due to immature immune systems. We report a case of a 1-year-old girl presenting with cough, fever, shortness of breath, and poor growth. Chest radiograph showed consolidation and infiltrates in the right paracardial area consistent with pulmonary tuberculosis. The patient was treated with first-line anti-tuberculosis drugs, including rifampicin and pyrazinamide, along with nutritional support, resulting in significant clinical improvement and discharge on day eight. This case emphasizes the importance of early recognition and management of tuberculosis in young children with risk factors such as malnutrition and incomplete immunization. Preventive strategies such as BCG vaccination, contact screening, family education, and access to tuberculosis preventive therapy are essential to reduce morbidity and mortality in childhood TB.

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near real time under simulated deployment conditions, continuously acquiring and analyzing physiological signals (respiratory rate, heart rate, SpO₂, and body temperature) alongside event-driven acoustic biomarkers (cough sounds) within a distributed architecture. A lightweight edge module performs local signal preprocessing and anomaly triage, selectively transmitting salient information to a cloud-based multimodal deep learning model for refined risk estimation and interpretability analysis. The framework was evaluated using a multi-source dataset comprising public repositories (MIMIC-III and Coswara) and a clinically supervised wearable study conducted in two Nigerian hospitals, resulting in 718  hours of quality-controlled multimodal monitoring data. In a pooled multi-source evaluation, the system achieved an AUC of 0.95, while in a clinically realistic local-only evaluation, the AUC was 0.86, reflecting a consistent but preliminary diagnostic signal. These results highlight the importance of local data adaptation for real-world applicability and suggest that multimodal AI can provide meaningful early risk indicators under resource constraints. Beyond predictive performance, this work demonstrates the feasibility of integrating multimodal learning, edge–cloud computation, and explainable analytics into a deployment-aware, privacy-preserving monitoring framework for low-resource healthcare environments.