Child abuse is a widespread global health crisis with longterm implications for children’s physical, psychological, and emotional well-being. Pediatric emergency departments (PEDs) are often the first point of contact for children experiencing abuse, presenting unique diagnostic challenges due to the diverse manifestations of maltreatment. In contrast to physical abuse that often leaves visible markers, nonphysical abuse (e.g., emotional neglect or psychological harm) frequently go undetected, exacerbating underreporting and delayed interventions. This article examines the prevalence, diagnostic challenges, and multidisciplinary strategies for addressing child maltreatment in PEDs. Cultural and systemic barriers, coupled with insufficient training and resource constraints, hinder the recognition of subtle signs of nonphysical abuse. Advances in diagnostic imaging, behavioral assessment tools, and laboratory investigations are discussed as critical components of comprehensive evaluations. Multidisciplinary teams play a pivotal role in addressing the complexity of abuse cases, combining medical, psychological, and legal expertise to provide holistic care while minimizing additional trauma. The integration of artificial intelligence into diagnostic workflows is explored as a transformative innovation, offering potential for early detection of abuse patterns and enhanced decision-making. However, the implementation of artificial intelligence requires careful consideration to ensure that it complements human expertise and aligns with ethical standards. There is a need for standardized protocols, targeted training programs, and supportive systems to empower healthcare professionals in recognizing and reporting all forms of child maltreatment. We propose more research to develop tools capable of detecting nonphysical abuse in PEDs. By combining technological advancements, multidisciplinary collaboration, and educational initiatives, PEDs can improve outcomes for vulnerable children and address the widespread issue of child maltreatment more effectively.

Child abuse is a widespread global health crisis with longterm implications for children’s physical, psychological, and emotional well-being. Pediatric emergency departments (PEDs) are often the first point of contact for children experiencing abuse, presenting unique diagnostic challenges due to the diverse manifestations of maltreatment. In contrast to physical abuse that often leaves visible markers, nonphysical abuse (e.g., emotional neglect or psychological harm) frequently go undetected, exacerbating underreporting and delayed interventions. This article examines the prevalence, diagnostic challenges, and multidisciplinary strategies for addressing child maltreatment in PEDs. Cultural and systemic barriers, coupled with insufficient training and resource constraints, hinder the recognition of subtle signs of nonphysical abuse. Advances in diagnostic imaging, behavioral assessment tools, and laboratory investigations are discussed as critical components of comprehensive evaluations. Multidisciplinary teams play a pivotal role in addressing the complexity of abuse cases, combining medical, psychological, and legal expertise to provide holistic care while minimizing additional trauma. The integration of artificial intelligence into diagnostic workflows is explored as a transformative innovation, offering potential for early detection of abuse patterns and enhanced decision-making. However, the implementation of artificial intelligence requires careful consideration to ensure that it complements human expertise and aligns with ethical standards. There is a need for standardized protocols, targeted training programs, and supportive systems to empower healthcare professionals in recognizing and reporting all forms of child maltreatment. We propose more research to develop tools capable of detecting nonphysical abuse in PEDs. By combining technological advancements, multidisciplinary collaboration, and educational initiatives, PEDs can improve outcomes for vulnerable children and address the widespread issue of child maltreatment more effectively.

Child abuse is a widespread global health crisis with longterm implications for children’s physical, psychological, and emotional well-being. Pediatric emergency departments (PEDs) are often the first point of contact for children experiencing abuse, presenting unique diagnostic challenges due to the diverse manifestations of maltreatment. In contrast to physical abuse that often leaves visible markers, nonphysical abuse (e.g., emotional neglect or psychological harm) frequently go undetected, exacerbating underreporting and delayed interventions. This article examines the prevalence, diagnostic challenges, and multidisciplinary strategies for addressing child maltreatment in PEDs. Cultural and systemic barriers, coupled with insufficient training and resource constraints, hinder the recognition of subtle signs of nonphysical abuse. Advances in diagnostic imaging, behavioral assessment tools, and laboratory investigations are discussed as critical components of comprehensive evaluations. Multidisciplinary teams play a pivotal role in addressing the complexity of abuse cases, combining medical, psychological, and legal expertise to provide holistic care while minimizing additional trauma. The integration of artificial intelligence into diagnostic workflows is explored as a transformative innovation, offering potential for early detection of abuse patterns and enhanced decision-making. However, the implementation of artificial intelligence requires careful consideration to ensure that it complements human expertise and aligns with ethical standards. There is a need for standardized protocols, targeted training programs, and supportive systems to empower healthcare professionals in recognizing and reporting all forms of child maltreatment. We propose more research to develop tools capable of detecting nonphysical abuse in PEDs. By combining technological advancements, multidisciplinary collaboration, and educational initiatives, PEDs can improve outcomes for vulnerable children and address the widespread issue of child maltreatment more effectively.

Child abuse is a widespread global health crisis with longterm implications for children’s physical, psychological, and emotional well-being. Pediatric emergency departments (PEDs) are often the first point of contact for children experiencing abuse, presenting unique diagnostic challenges due to the diverse manifestations of maltreatment. In contrast to physical abuse that often leaves visible markers, nonphysical abuse (e.g., emotional neglect or psychological harm) frequently go undetected, exacerbating underreporting and delayed interventions. This article examines the prevalence, diagnostic challenges, and multidisciplinary strategies for addressing child maltreatment in PEDs. Cultural and systemic barriers, coupled with insufficient training and resource constraints, hinder the recognition of subtle signs of nonphysical abuse. Advances in diagnostic imaging, behavioral assessment tools, and laboratory investigations are discussed as critical components of comprehensive evaluations. Multidisciplinary teams play a pivotal role in addressing the complexity of abuse cases, combining medical, psychological, and legal expertise to provide holistic care while minimizing additional trauma. The integration of artificial intelligence into diagnostic workflows is explored as a transformative innovation, offering potential for early detection of abuse patterns and enhanced decision-making. However, the implementation of artificial intelligence requires careful consideration to ensure that it complements human expertise and aligns with ethical standards. There is a need for standardized protocols, targeted training programs, and supportive systems to empower healthcare professionals in recognizing and reporting all forms of child maltreatment. We propose more research to develop tools capable of detecting nonphysical abuse in PEDs. By combining technological advancements, multidisciplinary collaboration, and educational initiatives, PEDs can improve outcomes for vulnerable children and address the widespread issue of child maltreatment more effectively.

Child abuse is a widespread global health crisis with longterm implications for children’s physical, psychological, and emotional well-being. Pediatric emergency departments (PEDs) are often the first point of contact for children experiencing abuse, presenting unique diagnostic challenges due to the diverse manifestations of maltreatment. In contrast to physical abuse that often leaves visible markers, nonphysical abuse (e.g., emotional neglect or psychological harm) frequently go undetected, exacerbating underreporting and delayed interventions. This article examines the prevalence, diagnostic challenges, and multidisciplinary strategies for addressing child maltreatment in PEDs. Cultural and systemic barriers, coupled with insufficient training and resource constraints, hinder the recognition of subtle signs of nonphysical abuse. Advances in diagnostic imaging, behavioral assessment tools, and laboratory investigations are discussed as critical components of comprehensive evaluations. Multidisciplinary teams play a pivotal role in addressing the complexity of abuse cases, combining medical, psychological, and legal expertise to provide holistic care while minimizing additional trauma. The integration of artificial intelligence into diagnostic workflows is explored as a transformative innovation, offering potential for early detection of abuse patterns and enhanced decision-making. However, the implementation of artificial intelligence requires careful consideration to ensure that it complements human expertise and aligns with ethical standards. There is a need for standardized protocols, targeted training programs, and supportive systems to empower healthcare professionals in recognizing and reporting all forms of child maltreatment. We propose more research to develop tools capable of detecting nonphysical abuse in PEDs. By combining technological advancements, multidisciplinary collaboration, and educational initiatives, PEDs can improve outcomes for vulnerable children and address the widespread issue of child maltreatment more effectively.

The current study aimed to determine the effect of silver and chitosan nanoparticles of size 10 to 30 nm on the dead of lice in vitro and in vivo to determine the optimal time and concentration to combat chicken lice. 100 local chickens Gallus gallus domesticus were collected from Al-Diwaniyah province and 6 species of local chicken lice were isolated: Menacanthus stramineus, Menacanthus pallidullus, Menacanthus cornutus, Goniodes gigas, Cuclotogaster heterographus and Bonomiella columbae. The results of treating lice with chitosan and silver nanoparticles at concentrations (40, 60, and 80) mg/mL in vitro and at different periods (5, 10, 15, and 30) minutes after treatment showed that chitosan and silver nanoparticles at a concentration of 80 mg/mL are the most effective in killing lice. The dead rate of lice reached 100% after 15 minutes of treatment with chitosan nanoparticles and 100% in the case of silver nanoparticles after 30 minutes. The results of spraying chitosan and silver nanoparticles on the body of chickens infected with lice experimentally, based on the relative therapeutic efficacy within 30 minutes, indicated that silver nanoparticles were the most effective in completely killing lice in the group treated with a concentration of 80 mg/kg after 30 minutes, where the percentage of therapeutic efficacy was 96.7%. This was followed by chitosan nanoparticles at a concentration of 80 mg/kg, and the percentage of therapeutic efficiency was 91.5%. Chitosan and silver nanocomposite have a promising effect in the elimination of lice infestation in chickens.

Background and Objectives: Tinnitus affects individuals’ quality of life, and multiple surveys have been conducted to assess this effect. One questionnaire used is the Tinnitus Primary Function Questionnaire. The aim of this study was to assess the reliability and validity of the Central Kurdish version of the Tinnitus Primary Function Questionnaire (TPFQ-CK). Subjects and Methods: We obtained permission from the originator of the TPFQ to proceed with this study. The English version was translated into Central Kurdish, following guidelines for the cross-cultural adaptation of health-related quality-of-life measures. A total of 205 participants who self-reported having subjective tinnitus for more than 3 months were included in this study. The TPFQ-CK and Tinnitus Handicap Questionnaire (THQ) were administered to all participants. Additionally, pure-tone audiometry was conducted. Results: Internal consistency of the TPFQ-CK was reflected in the total score (Cronbach’s alpha=0.933), and excellent consistency was present in test-retest reliability (intraclass correlation coefficient=1.000). The concordance correlation coefficient (concordance correlation coefficient=0.999) of the total TPFQ-CK score revealed high concordance and correlation between the two evaluations. The good construct validity of the TPFQ-CK was evidenced by the strong correlation (r=0.895) between the TPFQ-CK and THQ scores. Conclusions The TPFQ-CK is a valid and reliable assessment tool for evaluating the influence of tinnitus on the quality of life of Central Kurdish speakers with tinnitus.

This study aimsto determine the reliability and validity and optimal cut-off scores of the Malay version of Montreal Cognitive Assessment for visually impaired (MoCA-BM-blind) among older adults with cognitive decline. Participants for this study were recruited from the population-based longitudinal study on neuroprotective model for healthy longevity (LRGS TUA) among Malaysian older adults. A total of 220 participants, aged 60 years and above, from Selangor were included in this study. Cognitive functions were assessed using MoCA-BM-blind and Malay version of Mini-Mental State Examination for visually impaired (M-MMSE-blind). Habitual distance VA was measured monocularly using the Early Treatment Diabetic Retinopathy Study chart. Cronbach’s alpha and Pearson correlation coefficient were used to determine reliability and validity of MoCA-BM-blind, respectively. Receiver operating characteristic (ROC) analysis was conducted to determine the optimal cut-off score for MoCA-BM-blind. Reliability (Cronbach’s alpha: 0.76) and validity (Pearson correlation: r = 0.77) of MoCA-BM-blind were high. The optimal cutoff for MoCA-BM-blind in detecting cognitive decline was 9 with 86.8% sensitivity and 72.7% specificity. With this cut-off, 35.0% visually impaired participants were classified as having cognitive decline. MoCA-BM-blind is a valid and reliable screening tool to assess cognitive function among visually impaired older adults.
Aging