OBJECTIVE: To explore the application value of artificial intelligence (AI)-assisted chromosomal karyotype analysis in the diagnosis of prenatal chromosomal mosaicism. METHODS: A retrospective analysis was conducted on 172 pregnant women who underwent amniocentesis at the Department of Medical Genetics and Prenatal Diagnosis, the Third Affiliated Hospital of Zhengzhou University between January 2019 and December 2024. All cases whose fetuses were diagnosed with chromosomal mosaicism via karyotype analysis and stratified into two groups based on the analytical software employed: the conventional analysis group (n = 70), which utilized Leica analysis software for karyotype image recognition and cell counting; and the AI-assisted analysis group (n = 102), which utilized AI-assisted software for the same procedures. The clinical performance of AI-assisted karyotype analysis in diagnosing chromosomal mosaicism was comprehensively evaluated by comparing the types of mosaic karyotypes, distribution of mosaic ratios, and verification outcomes of different detection modalities between the two groups. This study was approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University (Ethics No.: 2024-406-01). RESULTS: No statistically significant difference was observed in baseline characteristics (maternal age, gestational week, and indications for prenatal diagnosis) between the two groups. Regarding the detection efficacy for numerical and structural mosaicisms, no significant difference was found in the detection of numerical mosaicism. However, the conventional analysis group exhibited a significantly higher detection rate of autosomal structural mosaicism compared to the AI-assisted group (11.43% vs. 0.98%, P < 0.05). Numerical mosaicism cases were further verified using copy number variation sequencing (CNV-seq) and/or fluorescence in situ hybridization (FISH). The AI-assisted group demonstrated a significantly lower inconsistency rate (5.56% vs. 20.41%, P < 0.05) compared to the conventional group. For low-proportion (< 10%) chromosomal mosaicism, the AI-assisted group had a significantly lower detection rate (13.25% vs. 29.69%, P < 0.05). Subsequent validation of low-proportion mosaicism by CNV-seq and/or FISH showed a higher consistency rate in the AI-assisted group (81.82% vs. 54.55%), though the difference did not reach statistical significance (P = 0.360). CONCLUSION For the karyotyping analysis of prenatal chromosomal mosaicism, AI-assisted karyotype analysis shows high accuracy and consistency in identifying numerical chromosomal mosaicism, particularly in reducing the detection of low-proportion (< 10%) mosaicism while improving verification accuracy. AI-assisted analysis can significantly improve the detection accuracy of numerical mosaicism and mitigate the risk of misclassification for low-proportion (< 10%) mosaicism, thereby providing more precise clinical evidence for the prenatal diagnosis of chromosomal mosaicisms.
Humans ; Female ; Mosaicism ; Pregnancy ; Karyotyping/methods* ; Artificial Intelligence ; Prenatal Diagnosis/methods* ; Adult ; Retrospective Studies ; Chromosome Disorders/genetics* ; Amniocentesis

OBJECTIVE: To explore the efficacy of a Thalassemia risk assessment software for the screening of thalassemia mutation carriers and distribution of thalassemia genotypes detected by screening. METHODS: A total of 6 040 individuals were evaluated at Leshan Maternal and Child Health Care Hospital between 2022 and 2024 using the commonly used clinical thalassemia risk assessment method and the thalassemia screening software, respectively, and the performance indicators of the two methods were compared and analyzed against the result of thalassemia gene testing. This study was approved by the Ethics Committee of our hospital (Ethics No.: LfyLL[2022]005). RESULTS: The high-risk rate by the thalassemia screening software was 11.19%, with a sensitivity of 95.12%, specificity of 93.28%, positive predictive value of 43.20%, negative predictive value of 99.72%, and the area under the ROC curve (AUC) was 0.942. The thalassemia gene detection rate of the high-risk samples screened was 4.83%. The high-risk screening rate of the conventional method was 2.50%, with a sensitivity of 51.22%, specificity of 93.28%, positive predictive value of 80.79%, negative predictive value of 97.40%, and the AUC was 0.754. The thalassemia gene detection rate of the high-risk samples was 2.02%. CONCLUSION The software can effectively detect thalassemia carriers and significantly reduce the missed detection compared with conventional method, thereby significantly improve the efficacy of screening.
Humans ; Thalassemia/diagnosis* ; Software ; Female ; Genetic Testing/methods* ; Male ; Mutation ; Adult ; Genotype ; ROC Curve ; Risk Assessment

Academic publishing is at a critical juncture. The challenges faced by the academics are mired in controversy. Among theseare three hotly debated concerns. First is the issue of whether technological innovations such as artificial intelligence (AI)improves research efficiency or if its use sacrifices research integrity.Another is the controversy between paywall publishingand open access. Lastly, adapting an appropriate business model for sustainability is a contentious issue and the choice betweena commercial or a university-based publishing platform is a difficult one.

Traditional models of scientific investigation relied on tedious intellectual calisthenics in all aspects of research —identifying research gaps, reviewing of published literature, devising valid methodology, collecting data, analysing results, and,finally, drawing conclusions. With the advent of powerful tools employing artificial intelligence, these heavy tasks are efficientlycarried out. The dilemma lies in determining which parts of the work can be attributed to the authors and which are ascribedto the output of large language models (LLMs) and other automated assistance employed.Despite requiring adequate vettingby experts of these AI-aided output, many in the scientific community still question these methods. Can research employingAI be considered honest work? Will full disclosure answer doubts as to the integrity of the scientific work?

Indeed, LLMs just gather information that is already out there, albeit more efficiently. After all, science progresses bystanding on the shoulder of giants. AI makes such work comprehensive and efficient. Standing on those proverbial shoulders,however, require access to prior work, hence our next challenge in academic publishing--open access versus paid access.Paywalls limit the benefits of valuable research to institutions and universities with the capacity to pay. Excluded from these arethose from low resourced countries, with nations from the global south being affected disproportionately. Additionally, whilenumerous authors appreciate the features of open access as it improves their impact and visibility, many feel unduly burdenedsince the cost of publishing in this format is passed on to them.

This brings us to our third issue: who bears the cost of academic publishing? Indeed, it is a lucrative industry, generatingan annual revenue of US$19 billion and an estimated 40 percent profit margin. Many, however, find fault in this businessmodel as concerns about the profit motives of the commercial publishers far overshadow their sustainability goals.

How do we navigate this landscape of controversies? We, at the ACTA, as part of the community of scholars, would needto clarify our mission. Our goals for this publication should be consistent with our values. These values, such as scientific rigor,integrity, and accountability, should be reflected in our policies. We should be cognizant of the role we play in national scientificdiscourse while we endeavor to make an impact in the global scene. We are accountable to our stakeholders — nurturingearly career scholars, supplying evidence to health policymakers, and being accountable to those who provide resources tosustain us. This stewardship is essential so that ACTA will stand shoulder to shoulder with the giants on which science buildsupon to benefit future generations.

Artificial Intelligence ; Commerce ; Costs And Cost Analysis ; Disclosure ; Drawing ; Efficiency ; Family Characteristics ; Forecasting ; Goals ; Gymnastics ; Health ; Health Resources ; Industry ; Intelligence ; Inventions ; Language ; Literature ; Methods ; Play And Playthings ; Policy ; Publications ; Publishing ; Research ; Residence Characteristics ; Role ; Science ; Shoulder ; Social Responsibility ; Universities ; Ursidae ; Volition ; Work ; World Health Organization

BACKGROUND

Early-stage endometrial cancer often presents with favorable survival rates, but high-risk factors, including TP53 mutations and high-grade serous pathology, can lead to recurrence and poor prognosis. The standard primary treatment for endometrial cancer is surgical staging, and lymph node metastases significantly impact adjuvant therapy decisions. The subgroup of p53-abnormal (p53abn) indicates the worst prognosis and potential benefits from adjuvant chemotherapy. Molecular classification, while recommended, faces practical challenges due to resource constraints.

OBJECTIVES

The study aimed to assess the incidence of p53 abnormal expression in clinical stage 1 endometrial cancer cases that underwent surgery at a government tertiary hospital, and assess its relationship with clinicopathologic factors and pelvic and paraaortic lymph node metastasis (LNM).

METHODS

A cross-sectional retrospective analysis was conducted on clinical early-stage endometrial cancer cases that underwent surgical primary treatment between January 2018 and December 2022. Patient records were reviewed to gather demographics, surgical information, and pathological evaluations. Preoperative clinical staging was determined through imaging, and surgical staging involved comprehensive lymphadenectomy. Immunohistochemistry studies for p53 were carried out on formalin-fixed paraffin-embedded tissue samples.

RESULTS

A total of 233 endometrial cancer cases were included. The mean age at diagnosis was 53.7 years. Common comorbidities included hypertension (47.2%) and dyslipidemia (20.6%). Most cases were endometrioid histology (82.8%) and low-grade tumors (85.8%). Tumor grade (p=0.010), myometrial invasion (pCONCLUSION

Tumor grade, myometrial invasion, and LVSI were all significantly associated with lymph node involvement. While p53 immunohistochemical stains show promise in predicting metastasis and has been associated with tumor aggressiveness, this should still be correlated with clinicopathological parameters to carry out a more accurate risk stratification of early-stage patients.

Therapeutics ; Survival Rate ; Risk Factors ; Recurrence ; Prognosis ; Pathology ; Endometrial Neoplasms ; Immunohistochemistry ; Tumor Suppressor Protein P53 ; Lymph Node Excision ; Risk Assessment

The growing use of generative artificial intelligence (AI) has coincided with more timely manuscript submissions and improved efficiency in scientific writing. While AI tools help authors produce clear and well-structured work, excessive or undisclosed reliance raises concerns about originality, authenticity, and the integrity of scholarly publications. Editors increasingly recognize AI-assisted writing and face the challenge of preserving rigorous standards. Emerging guidelines emphasize transparency in AI use, alongside the need to retain individual voice and diversity in scientific expression. This editorial highlights the balance between leveraging AI’s benefits and safeguarding ethical publication practices, while acknowledging contributors to the current issue.

Writing ; Work ; Voice ; Reference Standards ; Artificial Intelligence ; Intelligence ; Efficiency

This study aims to determine the prevalence of erectile dysfunction (ED) and identify its associated risk factors among young and middle-aged Filipino male patients diagnosed with diabetes mellitus. This study utilized a cross-sectional design to investigate the prevalence and associated factors of ED among male patients with diabetes. A total of 423 participants were recruited from clinical settings using purposive sampling. Data were collected using structured interviews and medical records, including demographics, comorbidities and laboratory results. Among 423 male diabetic patients, 78% were found to have ED. Patients with ED were significantly older (median: 49.5 versus 42 years, p<0.001), had higher body mass index (BMI), longer diabetes duration and more comorbidities. Univariable logistic regression showed that age (OR: 1.06, p<0.001), diabetes duration (OR: 1.11, p<0.001), hypertension (OR: 1.62, p = 0.042), dyslipidemia (OR: 1.75, p = 0.022), elevated HbA1c (>9.0%; OR: 3.43, p = 0.034) and serum creatinine (OR: 1.01, p = 0.008) were significantly associated with ED. However, none remained significant in the multivariable model. Male Filipino patients with diabetes have a significant burden of ED. Results of the univariable models show that age, duration of diabetes, hypertension, dyslipidemia, HbA1c and serum creatinine are significant individual predictors of ED.

Human ; Male ; Adolescent: 13-18 Yrs Old ; Young Adult: 19-24 Yrs Old ; Adult: 25-44 Yrs Old ; Middle Aged: 45-64 Yrs Old ; Aged: 65-79 Yrs Old ; Tertiary Care Centers ; Risk Factors ; Risk ; Medical Records ; Erectile Dysfunction ; Diabetes Mellitus ; Philippines

BACKGROUND

This study was inspired by the increasing cases of Diabetes Mellitus II (DM II) and the drive to strengthen early detection and intervention. The study specifically examined the American Diabetes Association (ADA) Risk Screening Form to detect DM and its potential as a cost-effective alternative to the standard screening criteria using Fasting Plasma Glucose (FPG) and 2-hour Oral Glucose Tolerance Test (OGTT).

METHODOLOGY

It utilized observational, cross sectional, descriptive, comparative design conducted among 269 hospital employees in a tertiary hospital. All participants were examined using the ADA Risk Screening Form and underwent standard test of FPG and 2-hour OGTT. Mean and standard deviation, frequency and percentage, and Mann-Whitney test were used in the treatment of data.

RESULTS

The clinicodemographic profile of the employees showed that most of the personnel belonged to age old (66.5%). Among the employees, 159 were females (66.5%) and 110 were males (59.1%). Additionally, most of the
personnel do not have hypertension (73.2%) and are physically active (56.1%) however most were noted to be overweight
(48.3%) and have family history of diabetes (45%). Furthermore, majority of the females did not have a history of gestational
diabetes mellitus (37.2%). The results revealed that most of the personnel were identified under decreased risk using the
ADA screening form and are non-diabetic (79.18%) using the laboratory test, whereas those pre-diabetic and diabetic
accounted 13.38% and 7.43% respectively. The results showed sensitivity of ADA Risk Screening Tool for DM Type 2
alongside the results of FPG and OGTT 30.4 and 25.7 respectively, specificity (87.3, 87.7), positive predictive value (38.6,
44.2), negative predictive value (82.7, 75.7), and accuracy (75.5, 70.6). Lastly, the results revealed that the use of ADA
screening tool showed no difference with the use of FPG with p-value of 0.095 and OGTT with p-value of 0.118.

Human ; Male ; Female ; Adult: 25-44 Yrs Old ; Middle Aged: 45-64 Yrs Old ; Association ; Form ; Government ; Hospitals ; Mass Screening ; Occupational Groups ; Risk

BACKGROUND

Diabetic retinopathy (DR) remains to be the leading cause of blindness worldwide. Traditionally, risk factors such as diabetes duration, HbA1c levels, hypertension and dyslipidemia have been closely linked to the development of this condition. However, recent research suggests that these factors account for only a portion of DR cases. Emerging studies highlight additional potential risk factors including diabetic nephropathy, insulin use, and body mass index -though data on these factors remain limited and at times contradictory. While there have been a few local studies exploring some of these variables, none have comprehensively examined how these factors collectively influence the occurrence and severity of diabetic retinopathy. This study aims to asses both the factors associated with presence and occurrence of diabetic retinopathy.

METHODOLOGY

This analytical cross-sectional study included patients with diabetes (n=201, aged 18 years and older) who underwent fundus photography at The Medical City Ortigas between January 1, 2022, and December 31, 2022. Data collection involved a thorough review of patient records, which provided demographic information and details on potential risk factors. The presence and severity of diabetic retinopathy (DR) were assessed based on fundus photography results, as interpreted by ophthalmologists. To evaluate the statistical significance of the association between risk factors and DR status, logistic regression analysis was done

RESULTS

Duration of diabetes (odds ratio [OR] 1.07; 95% CI, 1.01-1.13 per year increase), HBA1c (OR 1.4; 95% CI, 1.1-1.86),
fasting blood sugar (OR 1.4; 95% CI, 0.977-0.998), hypercholesterolemia (OR 5.02; 95% CI 1.67-16.44) and presence of
nephropathy (OR 3.39; 95% CI 1.49-8) were correlated with diabetic retinopathy.

CONCLUSION

The presence of diabetic retinopathy was significantly associated with several clinical factors. Each additional year of diabetes mellitus duration was associated with a 1.07-fold increase in the likelihood of DR. Additionally, each 1% increase in HbA1c was linked to a 1.40-fold increase in DR risk. The presence of diabetic nephropathy and hypercholesterolemia were also strong predictors, associated with a 3.39-fold and 5-fold increase in the likelihood of DR, respectively. Glycemic control, dyslipidemia and nephropathy appear to be associated with more severe forms of diabetic retinopathy. Clinicians handling diabetes patients with this patient profile should be wary of this diabetes complication.

Human ; Male ; Female ; Adolescent: 13-18 Yrs Old ; Young Adult: 19-24 Yrs Old ; Adult: 25-44 Yrs Old ; Diabetic Retinopathy ; Hospitals ; Risk ; Risk Factors ; Tertiary Care Centers

BACKGROUND

Early-stage endometrial cancer often presents with favorable survival rates, but high-risk factors, including TP53 mutations and high-grade serous pathology, can lead to recurrence and poor prognosis. The standard primary treatment for endometrial cancer is surgical staging, and lymph node metastases significantly impact adjuvant therapy decisions. The subgroup of p53-abnormal (p53abn) indicates the worst prognosis and potential benefits from adjuvant chemotherapy. Molecular classification, while recommended, faces practical challenges due to resource constraints.

OBJECTIVES

The study aimed to assess the incidence of p53 abnormal expression in clinical stage 1 endometrial cancer cases that underwent surgery at a government tertiary hospital, and assess its relationship with clinicopathologic factors and pelvic and paraaortic lymph node metastasis (LNM).

METHODS

A cross-sectional retrospective analysis was conducted on clinical early-stage endometrial cancer cases that underwent surgical primary treatment between January 2018 and December 2022. Patient records were reviewed to gather demographics, surgical information, and pathological evaluations. Preoperative clinical staging was determined through imaging, and surgical staging involved comprehensive lymphadenectomy. Immunohistochemistry studies for p53 were carried out on formalin-fixed paraffin-embedded tissue samples.

RESULTS

A total of 233 endometrial cancer cases were included. The mean age at diagnosis was 53.7 years. Common comorbidities included hypertension (47.2%) and dyslipidemia (20.6%). Most cases were endometrioid histology (82.8%) and low-grade tumors (85.8%). Tumor grade (p=0.010), myometrial invasion (pCONCLUSION

Tumor grade, myometrial invasion, and LVSI were all significantly associated with lymph node involvement. While p53 immunohistochemical stains show promise in predicting metastasis and has been associated with tumor aggressiveness, this should still be correlated with clinicopathological parameters to carry out a more accurate risk stratification of early-stage patients.

Therapeutics ; Survival Rate ; Risk Factors ; Recurrence ; Prognosis ; Pathology ; Endometrial Neoplasms ; Immunohistochemistry ; Tumor Suppressor Protein P53 ; Lymph Node Excision ; Risk Assessment

Artificial intelligence (AI) and generative artificial intelligence (GenAI) have gained increasing relevance in occupational therapy (OT) due to their potential to enhance clinical practice, optimize client care, and shape the future of OT education. Despite growing international evidence, literature addressing AI use in OT remains limited in the Philippine context. This Special Collection on AI in Occupational Therapy seeks to address this gap by examining the perspectives and experiences of key stakeholders across OT education and practice through a stakeholder-informed qualitative approach. Using interviews and focus group discussions, insights from school administrators, OT educators, interns, students, and clinicians are gathered to explore their attitudes, concerns, and lived experiences related to AI use in occupational therapy. These multi-perspective findings aim to inform the development of contextually grounded frameworks, institutional policies, and evidence-based programs that support ethical, sustainable, and meaningful integration of AI in OT education and practice within the local setting.

Human ; Artificial Intelligence ; Occupational Therapy ; Philippines