This special issue of the International Journal of Damage Mechanics is dedicated to Electronic Packaging. The issue contains papers by the leading researchers in the field. Using damage mechanics for electronic packaging problems is a new research area with a very promising future. Technical literature is full of papers on constitutive modeling of electronic packaging materials under monotonic loading. However, there are very few papers on damage mechanics of electronic packaging. The state-of-the-art practice of predicting number of cycles to failure of electronic packages is using empirical relations that are obtained from laboratory testing. Testing packages for fatigue life predictions is a very expensive and a time consuming process, especially for new designs. Damage mechanics offers a viable alternate procedure for predicting fatigue life of electronic packaging by means of computer simulations. This method is still unknown to many practicing engineers, and of course as with anything new, engineers who know of it don’t yet trust it. Hence, damage mechanics is rarely used by electronic packaging designers and manufacturers. But once there is enough academic research validating the method, practicing engineers will have confidence in it. It is my personal view that damage mechanics is going to be very popular in the industry in reducing dependence on testing as the sole means of predicting fatigue life. Computer simulations of reliability tests can significantly reduce the cost of developing new packages. In this endeavor, IJDM will be one of the primary publications to introduce the new developments.
First published in 1976,, the arguments made by Ivan Illich then are equally true today. ‘The medical establishment has become a major threat to health.’ This is the opening statement and basic contention of Ivan Illich’s searing social critique. In Limits to Medicine, Ivan Illich has enlarged on his theme of disabling social services, schools, and transport, which have become, through over-industrialization, harmful to man. In this radical contribution to social thinking, Illich decimates the myth of the magic of the medical profession. Illich argues that through iatrogenic diseases (ones that result from medical treatment), doctors can find themselves doing as much harm as good, and that there is a limit to the amount of medicine that a civilization should believe it is good to give.
Guillaume Balezo, Roger Trullo, Albert Pla Planas
et al.
Histopathological analysis is a cornerstone of cancer diagnosis, with Hematoxylin and Eosin (H&E) staining routinely acquired for every patient to visualize cell morphology and tissue architecture. On the other hand, multiplex immunofluorescence (mIF) enables more precise cell type identification via proteomic markers, but has yet to achieve widespread clinical adoption due to cost and logistical constraints. To bridge this gap, we introduce MIPHEI (Multiplex Immunofluorescence Prediction from H&E Images), a U-Net-inspired architecture that leverages a ViT pathology foundation model as encoder to predict mIF signals from H&E images using rich pretrained representations. MIPHEI targets a comprehensive panel of markers spanning nuclear content, immune lineages (T cells, B cells, myeloid), epithelium, stroma, vasculature, and proliferation. We train our model using the publicly available OrionCRC dataset of restained H&E and mIF images from colorectal cancer tissue, and validate it on five independent datasets: HEMIT, PathoCell, IMMUcan, Lizard and PanNuke. On OrionCRC test set, MIPHEI achieves accurate cell-type classification from H&E alone, with F1 scores of 0.93 for Pan-CK, 0.83 for alpha-SMA, 0.68 for CD3e, 0.36 for CD20, and 0.28 for CD68, substantially outperforming both a state-of-the-art baseline and a random classifier for most markers. Our results indicate that, for some molecular markers, our model captures the complex relationships between nuclear morphologies in their tissue context, as visible in H&E images and molecular markers defining specific cell types. MIPHEI offers a promising step toward enabling cell-type-aware analysis of large-scale H&E datasets, in view of uncovering relationships between spatial cellular organization and patient outcomes.
Ali Shadman Yazdi, Annalisa Cappella, Benedetta Baldini
et al.
Manual annotation of anatomical landmarks on 3D facial scans is a time-consuming and expertise-dependent task, yet it remains critical for clinical assessments, morphometric analysis, and craniofacial research. While several deep learning methods have been proposed for facial landmark localization, most focus on pseudo-landmarks or require complex input representations, limiting their clinical applicability. This study presents a fully automated deep learning pipeline (PAL-Net) for localizing 50 anatomical landmarks on stereo-photogrammetry facial models. The method combines coarse alignment, region-of-interest filtering, and an initial approximation of landmarks with a patch-based pointwise CNN enhanced by attention mechanisms. Trained and evaluated on 214 annotated scans from healthy adults, PAL-Net achieved a mean localization error of 3.686 mm and preserves relevant anatomical distances with a 2.822 mm average error, comparable to intra-observer variability. To assess generalization, the model was further evaluated on 700 subjects from the FaceScape dataset, achieving a point-wise error of 0.41\,mm and a distance-wise error of 0.38\,mm. Compared to existing methods, PAL-Net offers a favorable trade-off between accuracy and computational cost. While performance degrades in regions with poor mesh quality (e.g., ears, hairline), the method demonstrates consistent accuracy across most anatomical regions. PAL-Net generalizes effectively across datasets and facial regions, outperforming existing methods in both point-wise and structural evaluations. It provides a lightweight, scalable solution for high-throughput 3D anthropometric analysis, with potential to support clinical workflows and reduce reliance on manual annotation. Source code can be found at https://github.com/Ali5hadman/PAL-Net-A-Point-Wise-CNN-with-Patch-Attention
Vaskar Chakma, MD Jaheid Hasan Nerab, Abdur Rouf
et al.
Smoking continues to be a major preventable cause of death worldwide, affecting millions through damage to the heart, metabolism, liver, and kidneys. However, current medical screening methods often miss the early warning signs of smoking-related health problems, leading to late-stage diagnoses when treatment options become limited. This study presents a systematic comparative evaluation of machine learning approaches for smoking-related health risk assessment, emphasizing clinical interpretability and practical deployment over algorithmic innovation. We analyzed health screening data from 55,691 individuals, examining various health indicators, including body measurements, blood tests, and demographic information. We tested three advanced prediction algorithms - Random Forest, XGBoost, and LightGBM - to determine which could most accurately identify people at high risk. This study employed a cross-sectional design to classify current smoking status based on health screening biomarkers, not to predict future disease development. Our Random Forest model performed best, achieving an Area Under the Curve (AUC) of 0.926, meaning it could reliably distinguish between high-risk and lower-risk individuals. Using SHAP (SHapley Additive exPlanations) analysis to understand what the model was detecting, we found that key health markers played crucial roles in prediction: blood pressure levels, triglyceride concentrations, liver enzyme readings, and kidney function indicators (serum creatinine) were the strongest signals of declining health in smokers.
Whenty Ariyanti, Kai-Chun Liu, Kuan-Yu Chen
et al.
Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results.
Several therapy routines require deep breathing exercises as a key component and patients undergoing such therapies must perform these exercises regularly. Assessing the outcome of a therapy and tailoring its course necessitates monitoring a patient's compliance with the therapy. While therapy compliance monitoring is routine in a clinical environment, it is challenging to do in an at-home setting. This is so because a home setting lacks access to specialized equipment and skilled professionals needed to effectively monitor the performance of a therapy routine by a patient. For some types of therapies, these challenges can be addressed with the use of consumer-grade hardware, such as earphones and smartphones, as practical solutions. To accurately monitor breathing exercises using wireless earphones, this paper proposes a framework that has the potential for assessing a patient's compliance with an at-home therapy. The proposed system performs real-time detection of breathing phases and channels with high accuracy by processing a $\mathbf{500}$ ms audio signal through two convolutional neural networks. The first network, called a channel classifier, distinguishes between nasal and oral breathing, and a pause. The second network, called a phase classifier, determines whether the audio segment is from inhalation or exhalation. According to $k$-fold cross-validation, the channel and phase classifiers achieved a maximum F1 score of $\mathbf{97.99\%}$ and $\mathbf{89.46\%}$, respectively. The results demonstrate the potential of using commodity earphones for real-time breathing channel and phase detection for breathing therapy compliance monitoring.
Sasirangan is a typical cloth from the South Kalimantan which is produced by the Banjarist people in home industries. The production of sasirangan has a very positive impact on the welfare of Banjarist people. However, the processing and liquid waste resulting from the production process can have a negative impact on workers' health and the environment because it contains synthetic dyes and heavy metals. This is caused by poor worker behavior in processing and disposing of liquid waste from sasirangan cloth. Worker behavior can be related to worker education and knowledge. This research aims to analyze the relationship between education, knowledge and the behavior of sasirangan workers in processing and disposing of waste in the home-based sasirangan industry in Banjarmasin. This research is an analytical observational study with a cross sectional approach, carried out at 3 (three) sasirangan production locations: Sungai Jingah, Seberang Masjid Village and Surgi Mufti subdistricts. Sampling used a purposive sampling technique with a sample size of 30 workers. Data analysis was carried out using descriptive and statistical analysis using the Chi Square test with the alternative Fisher Exact Test. The results of data analysis show the p value of the education variables (p=0.032) and knowledge (p=0.049). There is a significant relationship between education and knowledge and worker behavior in processing and disposing of waste in the sasirangan home industry in Banjarmasin. This is in accordance with Lawrence Green's theory, the better the worker's education and knowledge, the better the worker's behavior. The existence of a significant relationship between education and knowledge and workers' behavior in processing and disposing of waste in the sasirangan home industry in Banjarmasin requires follow-up in the form of efforts to increase education and knowledge regarding the processing and disposal of sasirangan waste for workers in the sasirangan home industry in Banjarmasin.
Naser Sargolzaie, Vahid Reza Askari, Maryam Najjar
et al.
Objectives This study aimed to investigate the effect of propolis-based mouthwash on the gingival parameters of generalized chronic gingivitis cases in a randomized controlled clinical trial.
Methods A total number of 69 patients with generalized chronic gingivitis were randomly assigned into three groups (N=23): propolis, chlorhexidine, or placebo mouthwash. The gingival and bleeding indices were evaluated before and after two weeks of mouthwash use. Data analyses were performed using Kruskal-Wallis test, one-way analysis of variance, and paired t-test at p<0 .05.
Results The average gingival index in the chlorhexidine group was significantly higher than in the propolis group (p=0.005), but there was no significant difference between the placebo and propolis mouthwash groups (p=0.080). Moreover, the average plaque index was significantly higher in the chlorhexidine group than the propolis group (p<0.001). However, no significant difference was observed between the placebo and propolis mouthwash groups (p=0.742). However, the average bleeding index in the chlorhexidine group was significantly lower than the propolis group (p=0.012), with no significant difference between the placebo and propolis mouthwash groups (p=0.134).
Conclusion The present results showed that scaling and propolis mouthwash consumption significantly improved the bleeding on probing and the gingival indices compared to the placebo group. Therefore, this mouthwash can be useful for treating chronic generalized gingivitis.
The aim of this study is to identify the willingness of generation Z nursing students to work in the specialized units, as well as their views about these units, and to find answers to issues such as which units they want to work in and why. In this study, the phenomenological design of the qualitative research method was used. The purposive sampling technique was preferred in the sample selection of the study. The sample consisted of 16 students who completed their internal medicine nursing course and continued their education in the second year at Adıyaman University Faculty of Health Sciences. A semi-structured 13-item interview form was used as a data collection tool. The content analysis technique was used to analyze the data. Following the analysis, seven sub-themes were obtained. In the study, seven sub-themes were determined including explaining the reasons for specialized units, willingness to work in specialized units, choosing a particular specialized unit, getting support in deciding on the specialized units, the contribution of nursing education to deciding on the specialized units, research for specialized units, and physical opportunities in the specialized units. In accordance with the determined sub-themes, it was concluded that Z-generation nursing students desired to work in specialized units due to their professional development, improved patient care, more economic income, less violence towards healthcare workers in these units, as well as being informed by the lecturers, and sufficient physical opportunities. [Med-Science 2024; 13(1.000): 108-15]
Brinna Anindita, Department of Internal Medicine, Dr. Soetomo Teaching Hospital, Faculty of Medicine Universitas Airlangga, Surabaya, Indonesia, Titong Sugihartono
et al.
Inflammatory bowel disease (IBD) with a poor prognosis may be due to persistent colitis. According to the latest guidelines, monitoring has become a part of the treatment process for colitis. Adequate monitoring of the patient's condition is necessary to determine the course of the disease to prevent the worsening of the condition and suppress the subclinical inflammatory process. This analytical study with a cross-sectional design was conducted to evaluate the activity of colitis using the results of C-reactive protein (CRP) and fecal calprotectin (FC) assays. FC levels were analyzed by ELISA, while CRP levels were analyzed using Siemens Flex particle-enhanced turbidimetric immunoassay. In 30 subjects with endoscopy and biopsy of colitis, 16 men and 14 women had a median age of 52.5 (18–70) years. The median FC value increased by 67 (7.3–722 g/g) and was positive (≥50 g/g) in 20 subjects (66.7%), and the mean CRP value was 13.64 mg/L, positive (10–15 mg/L) in 13 subjects (43.33%), and negative (<10 mg/L) in 17 subjects (56.67%). This study demonstrated that FC had a significant relationship with CRP (r=0.57; p<0.001) in patients with colitis. Assessing the levels of FC and CRP among patients with colitis can be useful to assess the worsening of symptoms early and reduce mortality and morbidity.
Mohamed Abdelaziz Zaitri, Hanaa Zitane, Delfim F. M. Torres
We present a novel Pharmacokinetic/Pharmacodynamic (PK/PD) model for the induction phase of anesthesia, incorporating the $ψ$-Caputo fractional derivative. By employing the Picard iterative process, we derive a solution for a nonhomogeneous $ψ$-Caputo fractional system to characterize the dynamical behavior of the drugs distribution within a patient's body during the anesthesia process. To explore the dynamics of the fractional anesthesia model, we perform numerical analysis on solutions involving various functions of $ψ$ and fractional orders. All numerical simulations are conducted using the MATLAB computing environment. Our results suggest that the $ψ$ functions and the fractional order of differentiation have an important role in the modeling of individual-specific characteristics, taking into account the complex interplay between drug concentration and its effect on the human body. This innovative model serves to advance the understanding of personalized drug responses during anesthesia, paving the way for more precise and tailored approaches to anesthetic drug administration.
Background and aimsWnt/β-catenin signaling plays an important role in regulating hepatic metabolism. This study is to explore the molecular mechanisms underlying the potential crosstalk between Wnt/β-catenin and mTOR signaling in hepatic steatosis.MethodsTransgenic mice (overexpress Wnt1 in hepatocytes, Wnt+) mice and wild-type littermates were given high fat diet (HFD) for 12 weeks to induce hepatic steatosis. Mouse hepatocytes cells (AML12) and those transfected to cause constitutive β-catenin stabilization (S33Y) were treated with oleic acid for lipid accumulation.ResultsWnt+ mice developed more hepatic steatosis in response to HFD. Immunoblot shows a significant increase in the expression of fatty acid synthesis-related genes (SREBP-1 and its downstream targets ACC, AceCS1, and FASN) and a decrease in fatty acid oxidation gene (MCAD) in Wnt+ mice livers under HFD. Wnt+ mice also revealed increased Akt signaling and its downstream target gene mTOR in response to HFD. In vitro, increased lipid accumulation was detected in S33Y cells in response to oleic acid compared to AML12 cells reinforcing the in vivo findings. mTOR inhibition by rapamycin led to a down-regulation of fatty acid synthesis in S33Y cells. In addition, β-catenin has a physical interaction with mTOR as verified by co-immunoprecipitation in hepatocytes.ConclusionsTaken together, our results demonstrate that β-catenin stabilization through Wnt signaling serves a central role in lipid metabolism in the steatotic liver through up-regulation of fatty acid synthesis via Akt/mTOR signaling. These findings suggest hepatic Wnt signaling may represent a therapeutic strategy in hepatic steatosis.
Diseases of the endocrine glands. Clinical endocrinology
Ciro Gargiulo Isacco, Mario G. Balzanelli, Stefania Garzone
et al.
<i>Chlamydia trachomatis</i> and human papillomavirus (HPV) are the most common pathogens found in sexually transmitted infections (STIs), and both are known to increase the risk of cervical cancer (CC) and infertility. HPV is extremely common worldwide, and scientists use it to distinguish between low-risk and high-risk genotypes. In addition, HPV transmission can occur via simple contact in the genital area. From 50 to 80% of sexually active individuals become infected with both <i>C. trachomatis</i> and HPV viruses during their lifetime, and up to 50% become infected with an HPV oncogenic genotype. The natural history of this coinfection is strongly conditioned by the balance between the host microbiome and immune condition and the infecting agent. Though the infection often regresses, it tends to persist throughout adult life asymptomatically and silently. The partnership between HPV and <i>C. trachomatis</i> is basically due to their similarities: common transmission routes, reciprocal advantages, and the same risk factors. <i>C. trachomatis</i> is a Gram-negative bacteria, similar to HPV, and an intracellular bacterium, which shows a unique biphasic development that helps the latter continue its steady progression into the host throughout the entire life. Indeed, depending on the individual’s immune condition, the <i>C. trachomatis</i> infection tends to migrate toward the upper genital tract and spread to the uterus, and the fallopian tubes open up a pathway to HPV invasion. In addition, most HPV and <i>C. trachomatis</i> infections related to the female genital tract are facilitated by the decay of the first line of defense in the vaginal environment, which is constituted by a healthy vaginal microbiome that is characterized by a net equilibrium of all its components. Thus, the aim of this paper was to highlight the complexity and fragility of the vaginal microenvironment and accentuate the fundamental role of all elements and systems involved, including the <i>Lactobacillus</i> strains (<i>Lactobacillus gasseri, Lactobacillus jensenii, Lactobacillus crispatus</i>) and the immune–endocrine system, in preserving it from oncogenic mutation. Therefore, age, diet, and genetic predisposition together with an unspecific, persistent low-grade inflammatory state were found to be implicated in a high frequency and severity grade of disease, potentially resulting in pre-cancerous and cancerous cervical lesions.