OBJECTIVES

text-align: justify;" data-mce-style="text-align: justify;">This study aimed to describe the demographic profile, intervention sessions, goal attainment scaling (GAS), and health-related quality of life (HRQOL) of autistic children receiving speech and language therapy (SLT) in a higher educational institution in the Philippines.

text-align: justify;" data-mce-style="text-align: justify;">Deidentified data from 18 autistic children aged 4–16 years (mean=8.2; SD=2.9) who received SLT for two months were analyzed. Their demographic profile, intervention sessions, GAS scores, and generic HRQOL scores were documented.

text-align: justify;" data-mce-style="text-align: justify;">Most participants were school-age children (n=12; 66%) and were boys (n=14; 78%). After two months, the GAS scores of 11 participants (61%) increased by 1–2 points, whereas the scores of the remaining participants decreased (n=6; 33%) or did not change (n=1; 6%). Their mean generic HRQOL scores before and after SLT were 65.6 (SD=15.2) and 61.2 (SD=17.4), respectively.

text-align: justify;" data-mce-style="text-align: justify;">While the GAS scores increased for most participants, their generic HRQOL scores did not show clinically significant changes after two months of SLT. This can be attributed to the few therapy sessions and short follow-up period. The findings highlight the need to provide long-term support to SLT services of autistic children in the Philippines to document more desirable quality of life outcomes.

Language ; Pathology ; Speech ; Speech-language Pathology ; Weights And Measures

Human ; Writing ; Artificial Intelligence

text-align: justify;" data-mce-style="text-align: justify;">Case reports remain to be an essential part of knowledge generation in health care. It is a research design that involves writing about a patient’s illness with either an unusual, new, unexpected, or unique characteristic. It can be about new findings, a novel diagnostic test, unfamiliar adverse events or innovative medical and surgical interventions. It is a detailed description of a patient’s course of illness including symptoms, physical examination findings, laboratory results, treatment modalities and outcomes. The essential element of writing a case report or series is to contribute to the generation of new knowledge. Wellwritten manuscripts have a valuable purpose in medicine as they present new illness, unexpected effects of treatment, novel diagnostic exams or unforeseen patients’ outcomes. The sections of a case report include an Abstract, Introduction or Background, Case Presentation [history, physical examinations, investigations or laboratories, differential diagnosis (if relevant), treatment (if relevant), outcome/follow-up, Discussion, Learning points/Take home messages, Patients perspectives and References. Manuscripts written as case reports or case series by nature of their design are not required to get approval from an Ethics Review Board (ERB). However, there should be an institutional process to clear and register papers. Case reports or a case series has its own distinctive writing components and features as not all single or series of clinical cases are reportable. This article aimed to define case reports/series, describe the different parts, how to write and evaluate a case report manuscript using the CARE guidelines.

Chinese medicine (CM) diagnosis intellectualization is one of the hotspots in the research of CM modernization. The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues, however, it is difficult to solve the problems such as excessive or similar categories. With the development of natural language processing techniques, text generation technique has become increasingly mature. In this study, we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues. The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory (BILSTM) with Transformer as the backbone network. Meanwhile, the CM diagnosis generation model Knowledge Graph Enhanced Transformer (KGET) was established by introducing the knowledge in medical field to enhance the inferential capability. The KGET model was established based on 566 CM case texts, and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence (LSTM-seq2seq), Bidirectional and Auto-Regression Transformer (BART), and Chinese Pre-trained Unbalanced Transformer (CPT), so as to analyze the model manifestations. Finally, the ablation experiments were performed to explore the influence of the optimized part on the KGET model. The results of Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2 and Edit distance of KGET model were 45.85, 73.93, 54.59 and 7.12, respectively in this study. Compared with LSTM-seq2seq, BART and CPT models, the KGET model was higher in BLEU, ROUGE1 and ROUGE2 by 6.00-17.09, 1.65-9.39 and 0.51-17.62, respectively, and lower in Edit distance by 0.47-3.21. The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance. Additionally, the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results. In conclusion, text generation technology can be effectively applied to CM diagnostic modeling. It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models. CM diagnostic text generation technology has broad application prospects in the future.
Humans ; Medicine, Chinese Traditional ; Pattern Recognition, Automated ; Asian People ; Language ; Learning

Background: Speech sound disorders (SSD) refer to difficulties in perceiving, mentally representing, and/or articulating speech sounds. In 2018, the Tagalog Sentence Repetition Test (SRT) was developed due to the lack of a commercially available local assessment tool for children with suspected SSDs. The SRT had not been validated or piloted yet. Objectives: This study aimed to determine the SRT’s content validity (comprehensiveness, relevance, comprehensibility), ability to successfully elicit the target sounds, and logistical feasibility and flaws. Methodology: All procedures were conducted online. Three linguists evaluated the comprehensiveness of the sounds covered, while 31 Manila Tagalog-speaking children (7 to 21 years old) participated in pilot testing. Post-testing, the children answered a questionnaire to evaluate their familiarity with the sentences’ words (relevance) and the comprehensibility of the test instructions. Content validity was assessed by computing the Content Validity Index (CVI). To see how well the test elicits the target sounds, the number of participants who produced each sound were computed. Results: A CVI of 1.0 was obtained for all aspects of content validity. All targets were produced by almost all the participants, except for the final glottal stop (18/31, 58%). The test administration seemed feasible as participants from all age groups successfully executed the task. Conclusion Although the SRT exhibited good content validity, some sentences need to be revised to address sound production issues noted during the pilot. This new version should be re-piloted to 7 to 11-year-olds in-person and via teleconferencing. A manual should also be created to facilitate administration.
Speech Disorders ; Speech Production Measurement

Purpose: The field of speech-language pathology (SLP) is a young profession in the Philippines compared and relative to the other health sciences in the country. The emergence of this profession is marked by the milestones laid by the development of the first speech pathology education and training program at the University of the Philippines (UP); the establishment of its national professional organization, the Philippine Association of Speech Pathologists (PASP); and the enactment of RA 11249 or the Speech Language Pathology Act, which created the Professional Regulatory Board for Speech-Language Pathology (PRB-SLP) under the Professional Regulation Commission (PRC). This article looks back at these early beginnings, focuses at the current status of the profession, and provides perspectives for its growth moving forward. Specifically, this article provides an overview of the education and training, professional organization, and local practice of Filipino SLPs. Some emerging issues about the local practice and research gaps are also discussed. Conclusions The SLP profession in the Philippines has come a long way in developing education and training programs, expanding its national professional organization, and obtaining regulation of the practice of this profession under the law. However, there is still much work to be done to ensure its growth and further its development as a health science. Among these, strengthening the body of research to respond to the evolving needs and distinct landscape of local practice could further the growth of SLP in the Philippines.
Speech-Language Pathology ; Philippines

text-align: justify;" data-mce-style="text-align: justify;">Conducting and writing quality improvement studies is a skill that every family and community physician should learn and apply in their practice. As family and community physicians it is one of our responsibilities to provide quality health care to our patients.1 Simply put, “quality” is doing the right thing right the first time and doing it better the next. Similar to the doctor-patient interaction in a typical consult, where the physician’s objective is to learn as much as possible about the patient’s signs and symptoms and medical history in order to make the right diagnosis and thus render the right treatment. The physician strives to do all this on the first visit and hopes to keep up-to-date on the condition so that when another patient presents with the same condition, he or she will receive better and more current care.

Antimicrobial peptides (AMPs) are small molecule peptides that are widely found in living organisms with broad-spectrum antibacterial activity and immunomodulatory effect. Due to slower emergence of resistance, excellent clinical potential and wide range of application, AMP is a strong alternative to conventional antibiotics. AMP recognition is a significant direction in the field of AMP research. The high cost, low efficiency and long period shortcomings of the wet experiment methods prevent it from meeting the need for the large-scale AMP recognition. Therefore, computer-aided identification methods are important supplements to AMP recognition approaches, and one of the key issues is how to improve the accuracy. Protein sequences could be approximated as a language composed of amino acids. Consequently, rich features may be extracted using natural language processing (NLP) techniques. In this paper, we combine the pre-trained model BERT and the fine-tuned structure Text-CNN in the field of NLP to model protein languages, develop an open-source available antimicrobial peptide recognition tool and conduct a comparison with other five published tools. The experimental results show that the optimization of the two-phase training approach brings an overall improvement in accuracy, sensitivity, specificity, and Matthew correlation coefficient, offering a novel approach for further research on AMP recognition.
Anti-Bacterial Agents/chemistry* ; Amino Acid Sequence ; Antimicrobial Cationic Peptides/chemistry* ; Antimicrobial Peptides ; Natural Language Processing

In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network's ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
Humans ; Colonic Polyps/diagnostic imaging* ; Computer Simulation ; Electric Power Supplies ; Semantics ; Image Processing, Computer-Assisted