On the Risks of Medicalization of Adolescents Self-Injuring Acts

Although common among community adolescents, self-injuring acts are mainly studied by psychiatrists and psychologists and rarely by social work researchers. The preponderance of medical research in the field has come to associate self-injuring acts with mental issues. This view has to a large extent been adopted among professionals as well as among laypeople. When examining adolescents’ unsolicited internet published narratives, this medicalization of self-injuring acts was found to have negative consequences for disclosure and help-seeking, and hence limit the adolescents’ possibilities to get adequate help and support. The main objective of this work is to study adolescents’ views on hampering factors for help-seeking for self-injuring acts and the role of medicalisation for their willingness for disclosure and help-seeking. Disclosure of self-injuring acts within the social network was described as met with demands to seek professional mental help. Seeking professional help was accompanied with fear of being perceived as crazy or diagnosed as mentally ill. Internet websites were described as value free and safe arenas giving opportunity to disclose self-injuring acts without fear of being stigmatized and labelled as mentally ill. An extended involvement of social work researchers and professionals, approaching self-injuring acts not primarily as a sign of mental problems, but as an adolescent way of trying to manage a complicated social context, could enhance finding adequate support systems. It is also necessary that the medical profession contributes to a demedicalization of self-injuring acts.

Non-medicalization of medical science:Rationalization for future

As we delve into the intricacies of human disease,millions of people continue to be diagnosed as having what are labelled as pre-conditions or sub-clinical entities and may receive treatments designed to prevent further progression to clinical disease,but with debatable impact and consequences.Endocrinology is no different,with almost every organ system and associated diseases having subclinical entities.Although the expansion of these“grey”pre-conditions and their treatments come with a better understanding of pathophysiologic processes,they also entail financial costs and drug adverse-effects,and lack true prevention,thus refuting the very foundation of Medicine laid by Hippocrates“Primum non nocere”(Latin),i.e.,do no harm.Subclinical hypothyroidism,prediabetes,osteopenia,and minimal autonomous cortisol excess are some of the endocrine preclinical conditions which do not require active pharmacological management in the vast majority.In fact,progression to clinical disease is seen in only a small minority with reversal to normality in most.Giving drugs also does not lead to true prevention by changing the course of future disease.The goal of the medical fraternity thus as a whole should be to bring this large chunk of humanity out of the hospitals towards leading a healthy lifestyle and away from the label of a medical disease condition.

Medicalization of Abortion in Poland: How Medicine Affects the Lives of Women

The process of medicalization of abortion in Poland began when pregnancy termination procedures were legalized in 1956.The context in which that was possible is important:it happened under the communist rule as part of the Soviet bloc.The main goal of communism was to promote scientific approach to medicine and to eliminate popular folk medicine.The communist rule was also characterized by state feminism,which involved mass employment of women in industry and other occupations.The positive side to the changes was the fact that health care was free of charge.However,the system excluded the care of village healers and abortionists who were replaced by obstetricians-gynecologists,usually men.According to the official propaganda,an abortion that was not performed by a medical professional was dangerous for a woman’s health and could cause her death.Indeed,abortion-related mortality decreased,but the rate of abortion itself did not fall;it gradually increased.This is typical for countries with no free market,including communist ones,where access to contraceptive pills is very limited with abortion being the primary method of birth control.After the fall of communism in Poland,access to abortion was severely restricted;nevertheless,contraceptive pills and morning-after pills are available on prescription from pharmacies.The total fertility rate decreased in comparison to the period of communism and its broad access to abortion.Therefore,I maintain that the process of medicalization of abortion has not ended despite the partial disenfranchisement of women.

Advancements in medical treatment for pancreatic neuroendocrine tumors:A beacon of hope

This editorial highlights the remarkable advancements in medical treatment strategies for pancreatic neuroendocrine tumors(pan-NETs),emphasizing tailored approaches for specific subtypes.Cytoreductive surgery and somatostatin analogs(SSAs)play pivotal roles in managing tumors,while palliative options such as molecular targeted therapy,peptide receptor radionuclide therapy,and chemotherapy are reserved for SSA-refractory patients.Gastrinomas,insul-inomas,glucagonomas,carcinoid tumors and VIPomas necessitate distinct thera-peutic strategies.Understanding the genetic basis of pan-NETs and exploring immunotherapies could lead to promising avenues for future research.This review underscores the evolving landscape of pan-NET treatment,offering renewed hope and improved outcomes for patients facing this complex disease.

A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of diabetes.At present,deep convolutional neural networks have achieved promising performance in automatic DR detection tasks.The convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for processing.However,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(e.g.hemorrhages and exudates)scattered throughout the whole image.The proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR detection.Patch-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as plaques.The Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained networks.Extensive experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

A cross-sectional study to assess medication safety,knowledge,attitude,and practices regarding nutrition and medication among pregnant women

Objective:To assess pregnant women's knowledge,attitude,and practice regarding nutrition and medication usage,analyse the prescribing pattern,and categorize them based on the Food and Drug Administration(FDA)guidelines.Methods:A cross-sectional study was conducted with 264 pregnant women in the obstetrics and gynaecology department of a tertiary care hospital from October 2022 to August 2023.A knowledge,attitude,and practice(KAP)questionnaire was prepared in English language by the researchers and validated by an expert panel consisting of 12 members.The validated questionnaire was then translated into regional languages,Kannada and Malayalam.The reliability of the questionnaire was assessed with test-retest method with a representative sample population of 30 subjects(10 subjects for each language).The subjects'knowledge,attitude,and practice were evaluated using the validated KAP questionnaire.The safety of the medication was assessed using the FDA drug safety classification for pregnancy.Results:The mean scores for nutritional and medication usage knowledge,attitude,and practice were 4.14±1.15,4.50±1.09,and 3.00±1.47,respectively.Among 30 prescribed medications,3 belong to category A(no risk in human studies),8 belong to category B(no risk in animal studies),18 belong to category C(risk cannot be ruled out)and 1 drug is not classified.A significant association was observed between medication knowledge and practice(r=0.159,P=0.010).Conclusions:Most of the study population knows the need to maintain good dietary and medication practices during pregnancy.Counselling pregnant women regarding diet and medication usage is crucial in maternal care.

Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

Mediating role of inner strength in the relationship between medication literacy and medication adherence among kidney transplant patients

Objective:Compared with long-term renal replacement therapy,kidney transplantation is the ideal treatment for end-stage renal disease(ESRD),significantly extending patient life and improving quality of life.Kidney transplant patients need to adhere to lifelong immunosuppressive medication regimens,but their medication adherence is generally poor compared with other organ transplant recipients.Medication adherence is closely related to medication literacy and psychological status,yet related studies are limited.This study aims to investigate the current status of medication adherence,inner strength,and medication literacy in kidney transplant patients,analyze the relationships among these 3 factors,and explore the mediating role of inner strength in the relationship between medication literacy and medication adherence.Methods:A cross-sectional survey was conducted from March to October 2023 involving 421 patients aged≥18 years who visited kidney transplantation outpatient clinics at 4 tertiary hospitals in Hunan Province.The inner strength,medication literacy,and medication adherence of kidney transplant patients were investigated using the Inner Strength Scale(ISS),the Chinese version of the Medication Literacy Assessment in Spanish and English(MedLitRxSE),and the Chinese version of the Morisky Medication Adherence Scale-8(C-MMAS-8),respectively.Univariate analysis was performed to examine the effects of demographic and clinical data on medication adherence.Correlation analysis was conducted to explore the relationships among medication literacy,medication adherence,and inner strength.Significant variables from univariate and correlation analyses were further analyzed using multiple linear regression,and the mediating effect of inner strength was explored.Results:Among the 421 questionnaires collected,408 were valid,with an effective rate of 96.91%.The scores of C-MMAS-8,MedLitRxSE,and ISS were 6.64±1.16,100.63±14.67,and 8.47±4.03,respectively.Among the 408 patients,only 86(21.08%)patients had a high level of medication adherence,whereas 230(56.37%)patients had a medium level of medication adherence,and 92(22.55%)patients had poor medication adherence.Univariate analysis indicated that the kidney transplant patients’age,marital status,education levels,years since their kidney transplant operation,number of hospitalizations after the kidney transplant,and adverse drug reactions showed significant differences in medication adherence(all P<0.05).Correlation analysis showed that inner strength positively correlated with both medication literacy(r=0.183,P<0.001)and medication adherence(r=0.201,P<0.001).Additionally,there was a positive correlation between medication adherence and medication literacy(r=0.236,P<0.001).Inner strength accounted for 13.22%of the total effect in the mediating role between medication literacy and medication adherence.Conclusion:The level of medication adherence among kidney transplant patients needs improvement,and targeted intervention measures are essential.Inner strength mediates the relationship between medication literacy and medication adherence in these patients.Healthcare professionals should focus on enhancing medication literacy and supporting patients’inner strength to improve medication adherence.

An Automatic Implementation of Oropharyngeal Swab Sampling for Diagnosing Respiratory Infectious Diseases via Soft Robotic End-Effectors

The most widely adopted method for diagnosing respiratory infectious diseases is to conduct polymerase chain reaction(PCR)assays on patients’respiratory specimens,which are collected through either nasal or oropharyngeal swabs.The manual swab sampling process poses a high risk to the examiner and may cause false-negative results owing to improper sampling.In this paper,we propose a pneumatically actuated soft end-effector specifically designed to achieve all of the tasks involved in swab sampling.The soft end-effector utilizes circumferential instability to ensure grasping stability,and exhibits several key properties,including high load-to-weight ratio,error tolerance,and variable swab-tip stiffness,leading to successful automatic robotic oropharyngeal swab sampling,from loosening and tightening the transport medium tube cap,holding the swab,and conducting sampling,to snapping off the swab tail and sterilizing itself.Using an industrial collaborative robotic arm,we integrated the soft end-effector,force sensor,camera,lights,and remote-control stick,and developed a robotic oropharyngeal swab sampling system.Using this swab sampling system,we conducted oropharyngeal swab-sampling tests on 20 volunteers.Our Digital PCR assay results(RNase P RNA gene absolute copy numbers for the samples)revealed that our system successfully collected sufficient numbers of cells from the pharyngeal wall for respiratory disease diagnosis.In summary,we have developed a pharyngeal swab-sampling system based on an“enveloping”soft actuator,studied the sampling process,and imple-mented whole-process robotic oropharyngeal swab-sampling.

Transfer force perception skills to robot‐assisted laminectomy via imitation learning from human demonstrations

A comparative study of two force perception skill learning approaches for robot‐assisted spinal surgery,the impedance model method and the imitation learning(IL)method,is presented.The impedance model method develops separate models for the surgeon and patient,incorporating spring‐damper and bone‐grinding models.Expert surgeons'feature parameters are collected and mapped using support vector regression and image navi-gation techniques.The imitation learning approach utilises long short‐term memory networks(LSTM)and addresses accurate data labelling challenges with custom models.Experimental results demonstrate skill recognition rates of 63.61%-74.62%for the impedance model approach,relying on manual feature extraction.Conversely,the imitation learning approach achieves a force perception recognition rate of 91.06%,outperforming the impedance model on curved bone surfaces.The findings demonstrate the potential of imitation learning to enhance skill acquisition in robot‐assisted spinal surgery by eliminating the laborious process of manual feature extraction.

Process,Material,and Regulatory Considerations for 3D Printed Medical Devices and Tissue Constructs

Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs.Each type of 3D printing technique has its unique advantages and limitations,and the selection of a suitable 3D printing technique is highly dependent on its intended application.In this review paper,we present and highlight some of the critical processes(printing parameters,build orientation,build location,and support structures),material(batch-to-batch consistency,recycling,protein adsorption,biocompatibility,and degradation properties),and regulatory considerations(sterility and mechanical properties)for 3D printing of personalized medical devices.The goal of this review paper is to provide the readers with a good understanding of the various key considerations(process,material,and regulatory)in 3D printing,which are critical for the fabrication of improved patient-specific 3D printed medical devices and tissue constructs.

Erratum to Treatment strategies for patients with HER2-positive gastric cancer

For the affiliation information,the affiliation for author Feixue Wang should be Department of GI Medical Oncology,Tianjin Medical University Cancer Institute and Hospital,National Clinical Research Center for Cancer,Tianjin's Clinical Research Center for Cancer,Tianjin Key Laboratory of Digestive Cancer,Key Laboratory of Cancer Prevention and Therapy,Tianjin Medical University,Tianjin 300060,China.

Medical management of urolithiasis: Great efforts and limited progress

Objective:To provide a comprehensive review on the existing literature on medical management of urolithiasis.Methods:A thorough literature review was performed using Medline,PubMed/PMC,Embase,and the Cochrane Database of Systematic Reviews up to December 2022 to identify publications on the medical management of urolithiasis.Studies that assessed dietary and pharmacologic management of urolithiasis were reviewed;studies on medical expulsive therapy were not included in this review.Results:Medical management of urolithiasis ranges from the prophylactic management of kidney stone disease to dissolution therapies.While most treatment concepts have been long established,large randomized controlled trials are scarce.Dietary modification and increased fluid intake remain cornerstones in the conservative management of urolithiasis.A major limitation for medical management of urolithiasis is poor patient compliance.Conclusion:Medical management of urolithiasis is more important in patients with recurrent urolithiasis and patients with metabolic abnormalities putting them at higher risk of developing stones.Although medical management can be effective in limiting stone recurrence,medical interventions often fail due to poor compliance.

EDSUCh:A robust ensemble data summarization method for effective medical diagnosis

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.

Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical professionals.To address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic mapping.Subsequently,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling point.The Daisy descriptor matrix for this point is then computed.Finally,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical image.This scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image watermarking.The embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational overhead.Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.

ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques accurately.Methods:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules.First,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask learning.Second,we propose the DualLoss function,tailored to the thyroid nodule localization and classification tasks.It balances the learning of the localization and classification tasks to help improve the model’s generalization ability.Third,we introduce strategies for augmenting the data.Finally,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid nodules.Results:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and TransUnet.Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules.It achieved improved accuracy of 3.9%and 1.5%,respectively.Conclusion:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks.Future research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample size.To tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD diagnosis.Firstly,the functional connectivity matrix was constructed to extract primary features.Then,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel sizes.Furthermore,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between subjects.After extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD patients.The proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application

This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network(FRHNN),utilizing memristors for emulating neural synapses.The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams,Lyapunov exponents(LEs),and bifurcation diagrams.Secondly,the parameter related firing behaviors are described through two-parameter bifurcation diagrams.Subsequently,local attraction basins reveal multi-stability phenomena related to initial values.Moreover,the proposed model is implemented on a microcomputer-based ARM platform,and the experimental results correspond to the numerical simulations.Finally,the article explores the application of digital watermarking for medical images,illustrating its features of excellent imperceptibility,extensive key space,and robustness against attacks including noise and cropping.

Report on Cardiovascular Health and Diseases in China 2023:An Updated Summary

Since 1990,China has made considerable progress in resolving the problem of“treatment difficulty”of cardiovascular diseases(CVD).The prevalent unhealthy lifestyle among Chinese residents has exposed a massive proportion of the population to CVD risk factors,and this situation is further worsened due to the accelerated aging population in China.CVD remains one of the greatest threats to the health of Chinese residents.In terms of the proportions of disease mortality among urban and rural residents in China,CVD has persistently ranked first.In 2021,CVD accounted for 48.98%and 47.35%of deaths in rural and urban areas,respectively.Two out of every five deaths can be attributed to CVD.To implement a national policy“focusing on the primary health institute and emphasizing prevention”and truly achieve a shift of CVD prevention and treatment from hospitals to communities,the National Center for Cardiovascular Diseases has organized experts from relevant fields across China to compile the“Report on Cardiovascular Health and Diseases in China”annually since 2005.The 2024 report is established based on representative,published,and high-quality big-data research results from cross-sectional and cohort population epidemiological surveys,randomized controlled clinical trials,large sample registry studies,and typical community prevention and treatment cases,along with data from some projects undertaken by the National Center for Cardiovascular Diseases.These firsthand data not only enrich the content of the current report but also provide a more timely and comprehensive reflection of the status of CVD prevention and treatment in China.

Yield of alarm features in predicting significant endoscopic findings among hospitalized patients with dyspepsia

BACKGROUND Dyspepsia is a very prevalent upper gastrointestinal tract symptoms complex.Some of these symptoms might arise from serious underlying diseases,so the promotion of evidence-based guidelines could potentially better align evaluation and treatment.AIM To determine the value of alarm features as a predictive factor for significant endoscopic findings(SEFs)among hospitalized patients presenting with dyspepsia.METHODS We conducted a retrospective case-control study including information about 6208 endoscopic procedures performed for hospitalized patients.Patients were divided into two groups,with and without SEFs,and compared to elucidate the ability of the different alarm features to predict SEFs.RESULTS During the study,605 patients fulfilled the inclusion criteria.When the demographics and clinical characteristics of the two groups were compared,tachycardia(P<0.05),normocytic anemia,(P<0.05),leukocytosis(P<0.05),and hypoalbuminemia(P<0.05)documented on admission prior to endoscopy were strong predictors of SEFs.Among the alarm features,upper gastrointestinal bleeding,persistent vomiting,odynophagia[odds ratio(OR)=3.81,P<0.05;OR=1.75,P=0.03;and OR=7.81,P=0.07,respectively]were associated with SEFs.Unexplained weight loss was strongly associated with malignancy as an endoscopic finding(OR=2.05;P<0.05).In addition,long-term use of anti-aggregate medications other than aspirin(P<0.05)was correlated to SEFs.CONCLUSION Novel predictors of SEFs were elucidated in this study.These parameters could be used as an adjunctive in decision making regarding performing upper endoscopy in hospitalized patients with dyspepsia.