Leveraging Artificial Intelligence and Machine Learning to Optimize Enhanced Recovery After Surgery (ERAS) Protocols

Enhanced recovery after surgery (ERAS) protocols have transformed perioperative care by implementing evidence-based strategies to hasten patient recovery, decrease complications, and shorten hospital stays. However, challenges such as inconsistent adherence and the need for personalized adjustments persist, prompting exploration into innovative solutions. The emergence of artificial intelligence (AI) and machine learning (ML) offers a promising avenue for optimizing ERAS protocols. While ERAS emphasizes preoperative optimization, minimally invasive surgery (MIS), and standardized postoperative care, challenges such as adherence variability and resource constraints impede its effectiveness. AI/ML technologies offer opportunities to overcome these challenges by enabling real-time risk prediction, personalized interventions, and efficient resource allocation. AI/ML applications in ERAS extend to patient risk stratification, personalized care plans, and outcome prediction. By analyzing extensive patient datasets, AI/ML algorithms can predict individual patient risks and tailor interventions accordingly. Moreover, AI/ML facilitates proactive interventions through predictive modeling of postoperative outcomes, optimizing resource allocation, and enhancing patient care. Despite the potential benefits, integrating AI and ML into ERAS protocols faces obstacles such as data access, ethical considerations, and healthcare professional training. Overcoming these challenges requires a human-centered approach, fostering collaboration among clinicians, data scientists, and patients. Transparent communication, robust cybersecurity measures, and ethical model validation are crucial for successful integration. It is essential to ensure that AI and ML complement rather than replace human expertise, with clinicians maintaining oversight and accountability.

Laparoscopic Distal Pancreatectomy Using Three-Dimensional Computer Graphics for Surgical Navigation With a Deep Learning Algorithm: A Case Report

We have demonstrated the utility of SYNAPSE VINCENT® (version 6.6; Fujifilm Medical Co., Ltd., Tokyo, Japan), a 3D image analysis system, in semi-automated simulations of the peripancreatic vessels, pancreatic ducts, pancreatic parenchyma, and peripancreatic organs using an artificial intelligence (AI) engine developed with deep learning algorithms. Furthermore, we investigated the usefulness of this AI engine for patients with pancreatic cancer. Here, we present a case of laparoscopic distal pancreatectomy with an extended surgical procedure performed using surgical simulation and navigation via an AI engine. An 80-year-old woman presented with abdominal pain. Enhanced abdominal computed tomography (CT) revealed main pancreatic duct dilatation with a maximum diameter of 40 mm. Furthermore, there was a 17 mm cystic lesion between the pancreatic head and the pancreatic body and a 14 mm mural nodule in the pancreatic tail. Thus, the lesion was preoperatively diagnosed as an intraductal papillary carcinoma (IPMC) of the pancreatic tail and classified as T1N0M0 stage IA according to the 8th edition of the Union for International Cancer Control guidelines. The present patient had laparoscopic distal pancreatectomy and regional lymphadenectomy. In particular, since it was necessary to include the cystic lesion in the pancreatic neck, pancreatic resection was performed at the right edge of the portal vein, which is closer to the head of the pancreas than usual. We routinely employed three-dimensional computer graphics (3DCG) surgical simulation and navigation, which allowed us to recognize the surgical anatomy, including the location of pancreatic resection. In addition to displaying the detailed 3DCG of the surgical anatomy, this technology allowed surgical staff to share the situation, and it has been reported that this approach improves the safety of surgery. Furthermore, the remnant pancreatic volume (47.6%), pancreatic resection surface area (161 mm2), and thickness of the pancreatic parenchyma (12 mm) at the resection location were investigated using 3DCG imaging. Intraoperative frozen biopsy confirmed that the resection margin was negative. Histologically, an intraductal papillary mucinous neoplasm with low-grade dysplasia was observed in the pancreatic tail. No malignant findings, including those related to the resection margin, were observed in the specimen. At the 12-month postoperative follow-up examination, the patient's condition was unremarkable. We conclude that the SYNAPSE VINCENT® AI engine is a useful surgical support for the extraction of the surrounding vessels, surrounding organs, and pancreatic parenchyma including the location of the pancreatic resection even in the case of extended surgical procedures.

Rogue AI: Cautionary Cases in Neuroradiology and What We Can Learn From Them

Introduction In recent years, artificial intelligence (AI) in medical imaging has undergone unprecedented innovation and advancement, sparking a revolutionary transformation in healthcare. The field of radiology is particularly implicated, as clinical radiologists are expected to interpret an ever-increasing number of complex cases in record time. Machine learning software purchased by our institution is expected to help our radiologists come to a more prompt diagnosis by delivering point-of-care quantitative analysis of suspicious findings and streamlining clinical workflow. This paper explores AI's impact on neuroradiology, an area accounting for a substantial portion of recent radiology studies. We present a case series evaluating an AI software's performance in detecting neurovascular findings, highlighting five cases where AI interpretations differed from radiologists' assessments. Our study underscores common pitfalls of AI in the context of CT head angiograms, aiming to guide future AI algorithms.  Methods We conducted a retrospective case series study at Stony Brook University Hospital, a large medical center in Stony Brook, New York, spanning from October 1, 2021 to December 31, 2021, analyzing 140 randomly sampled CT angiograms using AI software. This software assessed various neurovascular parameters, and AI findings were compared with neuroradiologists' interpretations. Five cases with divergent interpretations were selected for detailed analysis. Results Five representative cases in which AI findings were discordant with radiologists' interpretations are presented with diagnoses including diffuse anoxic ischemic injury, cortical laminar necrosis, colloid cyst, right superficial temporal artery-to-middle cerebral artery (STA-MCA) bypass, and subacute bilateral subdural hematomas. Discussion The errors identified in our case series expose AI's limitations in radiology. Our case series reveals that AI's incorrect interpretations can stem from complexities in pathology, challenges in distinguishing densities, inability to identify artifacts, identifying post-surgical changes in normal anatomy, sensitivity limitations, and insufficient pattern recognition. AI's potential for improvement lies in refining its algorithms to effectively recognize and differentiate pathologies. Incorporating more diverse training datasets, multimodal data, deep-reinforcement learning, clinical context, and real-time learning capabilities are some ways to improve AI's performance in the field of radiology. Conclusion Overall, it is apparent that AI applications in radiology have much room for improvement before becoming more widely integrated into clinical workflows. While AI demonstrates remarkable potential to aid in diagnosis and streamline workflows, our case series highlights common pitfalls that underscore the need for continuous improvement. By refining algorithms, incorporating diverse datasets, embracing multimodal information, and leveraging innovative machine learning strategies, AI's diagnostic accuracy can be significantly improved.

Artificial Intelligence and Digital Divide in Physiotherapy Education

The potential of artificial intelligence (AI) in health care and education has become increasingly evident, promising to revolutionize how healthcare professionals deliver services and how learners engage with educational content. AI enhances individualized student learning experiences and transforms education delivery by adapting to emerging healthcare advancements. We emphasize the current need for more exploration of AI's applications in day-to-day education in physiotherapy schools. We conducted a PubMed search, revealing a significant gap in research on AI in physiotherapy education compared to medical and dental education. Knowledge gaps and varied perspectives among Canadian healthcare students, including physiotherapy students, highlight the need for targeted educational strategies and ethical considerations. We conclude with a call to bridge the digital divide in physiotherapy education, stressing the importance of integrating AI to empower students and foster innovation in physiotherapy education.

A double-blinded study for quantifiable assessment of the diagnostic accuracy of AI tool "ADVEN-i" in identifying diseased fundus images including diabetic retinopathy on a retrospective data

PURPOSE

To quantifiably assess the diagnostic accuracy of Adven-I, a proprietary artificial intelligence (AI)-driven diagnostic system that automatically detects diseases from fundus images. The purpose is to quantify the performance of Adven-i in differentiating a nonreferable (within normal limits) image from a referable (diseased fundus) image and further segregating diabetic retinopathy (DR) from the rest of the abnormalities (non-DR) encompassing the wide spectrum of abnormal pathologies. The assessment is carried out in comparison to manual reading as the reference gold standard. Adven-i is the only AI system classifying retinal abnormalities into DR and non-DR classes separately, apart from predicting nonreferable fundus, while most existing systems classify fundus images into referable and nonreferable DR.

METHODS

The double-blinded study was conducted on retrospective data collected over the course of a year in the ophthalmology outpatient department (OPD) at a top Tier II eyecare hospital in Chandigarh, India. Three vitreoretina specialists who were blinded to one another read the images. The ground-truth was generated on the basis of majority agreement among the readers. An arbitrator's decision was regarded final if all three readers disagreed.

RESULTS

2261 fundus images were analyzed by Adven-i. The sensitivity and specificity of Adven-i in diagnosing images with abnormalities were 95.12% and 85.77%, respectively, and for segregating DR from rest of the retinal abnormalities were 91.87% and 85.12%, respectively.

CONCLUSIONS AND RELEVANCE

Adven-i shows definite promise in automated screening for early diagnosis of referable fundus images including DR. Adven-i can be adopted to scale for mass screening in resource-limited settings.

AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model

Introduction Ensuring access to high-quality information is paramount to facilitating informed surgical decision-making. The use of the internet to access health-related information is increasing, along with the growing prevalence of AI language models such as ChatGPT. We aim to assess the standard of AI-generated patient-facing information through a qualitative analysis of its readability and quality. Materials and methods We performed a retrospective qualitative analysis of information regarding three common vascular procedures: endovascular aortic repair (EVAR), endovenous laser ablation (EVLA), and femoro-popliteal bypass (FPBP). The ChatGPT responses were compared to patient information leaflets provided by the vascular charity, Circulation Foundation UK. Readability was assessed using four readability scores: the Flesch-Kincaid reading ease (FKRE) score, the Flesch-Kincaid grade level (FKGL), the Gunning fog score (GFS), and the simple measure of gobbledygook (SMOG) index. Quality was assessed using the DISCERN tool by two independent assessors. Results The mean FKRE score was 33.3, compared to 59.1 for the information provided by the Circulation Foundation (SD=14.5, p=0.025) indicating poor readability of AI-generated information. The FFKGL indicated that the expected grade of students likely to read and understand ChatGPT responses was consistently higher than compared to information leaflets at 12.7 vs. 9.4 (SD=1.9, p=0.002). Two metrics measure readability in terms of the number of years of education required to understand a piece of writing: the GFS and SMOG. Both scores indicated that AI-generated answers were less accessible. The GFS for ChatGPT-provided information was 16.7 years versus 12.8 years for the leaflets (SD=2.2, p=0.002) and the SMOG index scores were 12.2 and 9.4 years for ChatGPT and the patient information leaflets, respectively (SD=1.7, p=0.001). The DISCERN scores were consistently higher in human-generated patient information leaflets compared to AI-generated information across all procedures; the mean score for the information provided by ChatGPT was 50.3 vs. 56.0 for the Circulation Foundation information leaflets (SD=3.38, p<0.001). Conclusion We concluded that AI-generated information about vascular surgical procedures is currently poor in both the readability of text and the quality of information. Patients should be directed to reputable, human-generated information sources from trusted professional bodies to supplement direct education from the clinician during the pre-procedure consultation process.

Radiologists' Expectations of Artificial Intelligence in Pancreatic Cancer Imaging: How Good Is Good Enough?

BACKGROUND

Existing (artificial intelligence [AI]) tools in radiology are modeled without necessarily considering the expectations and experience of the end user-the radiologist. The literature is scarce on the tangible parameters that AI capabilities need to meet for radiologists to consider them useful tools.

OBJECTIVE

The purpose of this study is to explore radiologists' attitudes toward AI tools in pancreatic cancer imaging and to quantitatively assess their expectations of these tools.

METHODS

A link to the survey was posted on the www.ctisus.com website, advertised in the www.ctisus.com email newsletter, and publicized on LinkedIn, Facebook, and Twitter accounts. This survey asked participants about their demographics, practice, and current attitudes toward AI. They were also asked about their expectations of what constitutes a clinically useful AI tool. The survey consisted of 17 questions, which included 9 multiple choice questions, 2 Likert scale questions, 4 binary (yes/no) questions, 1 rank order question, and 1 free text question.

RESULTS

A total of 161 respondents completed the survey, yielding a response rate of 46.3% of the total 348 clicks on the survey link. The minimum acceptable sensitivity of an AI program for the detection of pancreatic cancer chosen by most respondents was either 90% or 95% at a specificity of 95%. The minimum size of pancreatic cancer that most respondents would find an AI useful at detecting was 5 mm. Respondents preferred AI tools that demonstrated greater sensitivity over those with greater specificity. Over half of respondents anticipated incorporating AI tools into their clinical practice within the next 5 years.

CONCLUSION

Radiologists are open to the idea of integrating AI-based tools and have high expectations regarding the performance of these tools. Consideration of radiologists' input is important to contextualize expectations and optimize clinical adoption of existing and future AI tools.

The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review

Implant dentistry has witnessed a transformative shift with the integration of artificial intelligence (AI) technologies. This article explores the role of AI in implant dentistry, emphasizing its impact on diagnostics, treatment planning, and patient outcomes. AI-driven image analysis and deep learning algorithms enhance the precision of implant placement, reducing risks and optimizing aesthetics. Moreover, AI-driven data analytics provide valuable insights into patient-specific treatment strategies, improving overall success rates. As AI continues to evolve, it promises to reshape the landscape of implant dentistry and lead in an era of personalized and efficient oral healthcare.

Revolutionizing Dental Care: A Comprehensive Review of Artificial Intelligence Applications Among Various Dental Specialties

Since the beginning of recorded history, the human brain has been one of the most intriguing structures for scientists and engineers. Over the centuries, newer technologies have been developed based on principles that seek to mimic their functioning, but the creation of a machine that can think and behave like a human remains an unattainable fantasy. This idea is now known as "artificial intelligence". Dentistry has begun to experience the effects of artificial intelligence (AI). These include image enhancement for radiology, which improves the visibility of dental structures and facilitates disease diagnosis. AI has also been utilized for the identification of periapical lesions and root anatomy in endodontics, as well as for the diagnosis of periodontitis. This review is intended to provide a comprehensive overview of the use of AI in modern dentistry's numerous specialties. The relevant publications published between March 1987 and July 2023 were identified through an exhaustive search. Studies published in English were selected and included data regarding AI applications among various dental specialties. Dental practice involves more than just disease diagnosis, including correlation with clinical findings and administering treatment to patients. AI cannot replace dentists. However, a comprehensive understanding of AI concepts and techniques will be advantageous in the future. AI models for dental applications are currently being developed.

Advancements in Understanding Gastric Cancer: A Comprehensive Review

As a complex and difficult condition, gastric cancer (GC) continues to have a big impact on the world's health. The goal of this review article is to give a thorough summary of the most recent developments and research discoveries in the field of stomach cancer. The review discusses a wide range of topics, such as the epidemiology and risk factors for GC, molecular insights into its pathogenesis, the use of biomarkers in diagnosis and prognosis, current and novel therapeutic approaches, and the intriguing potential of immunotherapy. In addition, procedures for surgery, therapy strategies, and imaging modalities for diagnosis and staging are examined. The paper emphasizes how crucial it is to comprehend the tumor microenvironment and how it affects the course of the disease. Overall, this review provides a comprehensive assessment of the current body of knowledge, highlights research gaps, and suggests future lines of inquiry to enhance the treatment of GC.

Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field

Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.

Decoding Applications of Artificial Intelligence in Rheumatology

Artificial intelligence (AI) is not a newcomer in medicine. It has been employed for image analysis, disease diagnosis, drug discovery, and improving overall patient care. ChatGPT (Chat Generative Pre-trained Transformer, Inc., Delaware) has renewed interest and enthusiasm in artificial intelligence. Algorithms, machine learning, deep learning, and data analysis are some of the complex terminologies often encountered when health professionals try to learn AI. In this article, we try to review the practical applications of artificial intelligence in vernacular language in the fields of medicine and rheumatology in particular. From the standpoint of the everyday physician, we have endeavored to encapsulate the influence of AI on the cutting edge of medical practice and the potential revolutionary shift in the realm of rheumatology.

Standardized Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification

PURPOSE

Myocardial perfusion (MP) stress single-photon emission computed tomography (SPECT) is an established diagnostic test for patients suspected of coronary artery disease (CAD). Meanwhile, coronary artery calcification (CAC) scoring obtained from diagnostic CT is a highly sensitive test, offering incremental diagnostic information in identifying patients with significant CAD yet normal MP stress SPECT (MPSS) scans. However, after decades of wide utilization of MPSS, CAC is not commonly reimbursed (e.g. by the CMS), nor widely deployed in community settings. We studied the potential of complementary information deduced from the radiomics analysis of normal MPSS scans in predicting the CAC score.

METHODS

We collected data from 428 patients with normal (non-ischemic) MPSS (99mTc-sestamibi; consensus reading). A nuclear medicine physician verified iteratively reconstructed images (attenuation-corrected) to be free from fixed perfusion defects and artifactual attenuation. Three-dimensional images were automatically segmented into four regions of interest (ROIs), including myocardium and three vascular segments (left anterior descending [LAD]-left circumference [LCX]-right coronary artery [RCA]). We used our software package, standardized environment for radiomics analysis (SERA), to extract 487 radiomic features in compliance with the image biomarker standardization initiative (IBSI). Isotropic cubic voxels were discretized using fixed bin-number discretization (eight schemes). We first performed blind-to-outcome feature selection focusing on a priori usefulness, dynamic range, and redundancy of features. Subsequently, we performed univariate and multivariate machine learning analyses to predict CAC scores from i) selected radiomic features, ii) 10 clinical features, and iii) combined radiomics + clinical features. Univariate analysis invoked Spearman correlation with Benjamini-Hotchberg false-discovery correction. The multivariate analysis incorporated stepwise linear regression, where we randomly selected a 15% test set and divided the other 85% of data into 70% training and 30% validation sets. Training started from a constant (intercept) model, iteratively adding/removing features (stepwise regression), invoking the Akaike information criterion (AIC) to discourage overfitting. Validation was run similarly, except that the training output model was used as the initial model. We randomized training/validation sets 20 times, selecting the best model using log-likelihood for evaluation in the test set. Assessment in the test set was performed thoroughly by running the entire operation 50 times, subsequently employing Fisher's method to verify the significance of independent tests.

RESULTS

Unsupervised feature selection significantly reduced 8×487 features to 56. In univariate analysis, no feature survived the false-discovery rate (FDR) to directly correlate with CAC scores. Applying Fisher's method to the multivariate regression results demonstrated combining radiomics with the clinical features to enhance the significance of the prediction model across all cardiac segments.  Conclusions: Our standardized and statistically robust multivariate analysis demonstrated significant prediction of the CAC score for all cardiac segments when combining MPSS radiomic features with clinical features, suggesting radiomics analysis can add diagnostic or prognostic value to standard MPSS for wide clinical usage.

Workforce Crisis in Radiology in the UK and the Strategies to Deal With It: Is Artificial Intelligence the Saviour?

Radiology has seen rapid growth over the last few decades. Technological advances in equipment and computing have resulted in an explosion of new modalities and applications. However, this rapid expansion of capability and capacity has not been matched by a parallel growth in the number of radiologists. This has resulted in global shortages in the workforce, with the UK being one of the most affected countries. The UK National Health Service has been employing several conventional strategies to deal with the workforce situation with mixed success. The emergence of artificial intelligence (AI) tools that have the potential to increase efficiency and efficacy at various stages in radiology has made it possible for radiology departments to use new strategies and workflows that can offset workforce shortages to some extent. This review article discusses the current and projected radiology workforce situation in the UK and the various strategies to deal with it, including applications of AI in radiology. We highlight the benefits of AI tools in improving efficiency and patient safety. AI has a role along the patient's entire journey from the clinician requesting the appropriate radiological investigation, safe image acquisition, alerting the radiologists and clinicians about critical and life-threatening situations, cancer screening follow up, to generating meaningful radiology reports more efficiently. It has great potential in easing the workforce crisis and needs rapid adoption by radiology departments.

Assessment of the Perception and Worries of Saudi Healthcare Providers About the Application of Artificial Intelligence in Saudi Health Facilities

Objective This study is aimed at assessing the perception and worries of Saudi healthcare providers about the application of artificial intelligence (AI) in Saudi healthcare facilities. Methods The study adopted a cross-sectional study involving 1026 Saudi healthcare providers between January 2023 and April 2023. The target population was healthcare providers across Saudi health facilities. Online questionnaires were administered through social media platforms. Data were analyzed using SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY) to obtain important insights. Results The results of this study indicated that more than half (55.2%) of the respondents had good knowledge of AI, with (48.1%) of them being familiar with the application of AI in their specialty. A good proportion of the participants (57.9%) knew at least one term about the difference between machine learning and deep learning. More than half (69.9%) of the participants indicated that they had at one point in time used speech recognition or transcription application in their work. A large section (73.3%) of healthcare providers believed that AI would replace them at their job. A vast majority (84.9%) of the participants agreed that collaboration between medical schools with engineering and computer science faculties could be a game changer to provide a road for incorporating AI into medical curricula. The mean perception of AI in this study was 37.6 (SD=8.41; range 0-241). Age, level of health, health profession, and working experience all significantly impacted the positive perception score (p=0.021; p=0.031; p=0.041; p=0.026). However, there was no significant association between gender, nationality, and Saudi regions with a mean positive perception score. Conclusion There was a positive perception of AI among Saudi healthcare providers. Even though a substantial majority of Saudi healthcare providers were worried that AI would replace their jobs, the study revealed that AI serves as a crucial practitioner's tool rather than a physician's replacement.

Spatial cancer systems biology resolves heterotypic interactions and identifies disruption of spatial hierarchy as a pathological driver event

Spatially annotated single-cell datasets provide unprecedented opportunities to dissect cell-cell communication in development and disease. Heterotypic signaling includes interactions between different cell types and is well established in tissue development and spatial organization. Epithelial organization requires several different programs that are tightly regulated. Planar cell polarity (PCP) is the organization of epithelial cells along the planar axis, orthogonal to the apical-basal axis. Here, we investigate PCP factors and explore the implications of developmental regulators as malignant drivers. Utilizing cancer systems biology analysis, we derive a gene expression network for WNT-ligands (WNT) and their cognate frizzled (FZD) receptors in skin cutaneous melanoma. The profiles supported by unsupervised clustering of multiple-sequence alignments identify ligand-independent signaling and implications for metastatic progression based on the underpinning developmental spatial program. Omics studies and spatial biology connect developmental programs with oncological events and explain key spatial features of metastatic aggressiveness. Dysregulation of prominent PCP factors such as specific representatives of the WNT and FZD families in malignant melanoma recapitulates the development program of normal melanocytes but in an uncontrolled and disorganized fashion.

Investigation of the Molecular Evolution of Treg Suppression Mechanisms Indicates a Convergent Origin

Regulatory T cell (Treg) suppression of conventional T cells is a central mechanism that ensures immune system homeostasis. The exact time point of Treg emergence is still disputed. Furthermore, the time of Treg-mediated suppression mechanisms’ emergence has not been identified. It is not yet known whether Treg suppression mechanisms diverged from a single pathway or converged from several sources. We investigated the evolutionary history of Treg suppression pathways using various phylogenetic analysis tools. To ensure the conservation of function for investigated proteins, we augmented our study using nonhomology-based methods to predict protein functions among various investigated species and mined the literature for experimental evidence of functional convergence. Our results indicate that a minority of Treg suppressor mechanisms could be homologs of ancient conserved pathways. For example, CD73, an enzymatic pathway known to play an essential role in invertebrates, is highly conserved between invertebrates and vertebrates, with no evidence of positive selection (w = 0.48, p-value < 0.00001). Our findings indicate that Tregs utilize homologs of proteins that diverged in early vertebrates. However, our findings do not exclude the possibility of a more evolutionary pattern following the duplication degeneration−complementation (DDC) model. Ancestral sequence reconstruction showed that Treg suppression mechanism proteins do not belong to one family; rather, their emergence seems to follow a convergent evolutionary pattern.

The American Academy of Ophthalmology IRIS Registry (Intelligent Research In Sight): current and future state of big data analytics

PURPOSE OF REVIEW

To describe the drivers, development, and current state of the American Academy of Ophthalmology IRIS Registry (Intelligent Research In Sight), and analytics involving deidentified aggregate IRIS Registry data.

RECENT FINDINGS

The IRIS Registry has a core mission of quality improvement and reporting. In addition, analytic projects performed to date have included characterizing patient populations and diseases, incidence, and prevalence; clinical outcomes and complications; risk factors and effect modifiers; practice patterns; and trends over time. Pipeline projects include application of artificial intelligence and machine learning approaches for predictive modeling and analytics, disease mapping, detecting patterns and identifying cohorts, and optimizing treatment based on patient-specific characteristics.

SUMMARY

The IRIS Registry is the nation's largest single specialty clinical registry, with unique data elements specific to ophthalmology. It offers a wealth of opportunities involving big data analytics, including traditional inferential statistics as well as machine learning and artificial intelligence approaches scalable on massive amounts of data.

Progress of Advanced Devices and Internet of Things Systems as Enabling Technologies for Smart Homes and Health Care

In the Internet of Things (IoT) era, various devices (e.g., sensors, actuators, energy harvesters, etc.) and systems have been developed toward the realization of smart homes/buildings and personal health care. These advanced devices can be categorized into ambient devices and wearable devices based on their usage scenarios, to enable motion tracking, health monitoring, daily care, home automation, fall detection, intelligent interaction, assistance, living convenience, and security in smart homes. With the rapidly increasing number of such advanced devices and IoT systems, achieving fully self-sustained and multimodal intelligent systems is becoming more and more important to realize a sustainable and all-in-one smart home platform. Hence, in this Review, we systematically present the recent progress of the development of advanced materials, fabrication techniques, devices, and systems for enabling smart home and health care applications. First, advanced polymer, fiber, and fabric materials as well as their respective fabrication techniques for large-scale manufacturing are discussed. After that, functional devices classified into ambient devices (at home ambiance such as door, floor, table, chair, bed, toilet, window, wall, etc.) and wearable devices (on body parts such as finger, wrist, arm, throat, face, back, etc.) are presented for diverse monitoring and auxiliary applications. Next, the current developments of self-sustained systems and intelligent systems are reviewed in detail, indicating two promising research directions in this field. Last, conclusions and outlook pinpointed on the existing challenges and opportunities are provided for the research community to consider.

Data-Driven Methods for Accelerating Polymer Design

Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.

#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

ACM REFERENCE FORMAT

Abigail Dommer 1† , Lorenzo Casalino 1† , Fiona Kearns 1† , Mia Rosenfeld 1 , Nicholas Wauer 1 , Surl-Hee Ahn 1 , John Russo, 2 Sofia Oliveira 3 , Clare Morris 1 , AnthonyBogetti 4 , AndaTrifan 5,6 , Alexander Brace 5,7 , TerraSztain 1,8 , Austin Clyde 5,7 , Heng Ma 5 , Chakra Chennubhotla 4 , Hyungro Lee 9 , Matteo Turilli 9 , Syma Khalid 10 , Teresa Tamayo-Mendoza 11 , Matthew Welborn 11 , Anders Christensen 11 , Daniel G. A. Smith 11 , Zhuoran Qiao 12 , Sai Krishna Sirumalla 11 , Michael O'Connor 11 , Frederick Manby 11 , Anima Anandkumar 12,13 , David Hardy 6 , James Phillips 6 , Abraham Stern 13 , Josh Romero 13 , David Clark 13 , Mitchell Dorrell 14 , Tom Maiden 14 , Lei Huang 15 , John McCalpin 15 , Christo- pherWoods 3 , Alan Gray 13 , MattWilliams 3 , Bryan Barker 16 , HarindaRajapaksha 16 , Richard Pitts 16 , Tom Gibbs 13 , John Stone 6 , Daniel Zuckerman 2 *, Adrian Mulholland 3 *, Thomas MillerIII 11,12 *, ShantenuJha 9 *, Arvind Ramanathan 5 *, Lillian Chong 4 *, Rommie Amaro 1 *. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing '21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis . ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI.

Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective

SARS-CoV-2 has infected over ∼165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed ∼3.4 million people. Artificial Intelligence (AI) has shown to benefit in the biomedical image such as X-ray/Computed Tomography in diagnosis of ARDS, but there are limited AI-based systematic reviews (aiSR). The purpose of this study is to understand the Risk-of-Bias (RoB) in a non-randomized AI trial for handling ARDS using novel AtheroPoint-AI-Bias (AP(ai)Bias). Our hypothesis for acceptance of a study to be in low RoB must have a mean score of 80% in a study. Using the PRISMA model, 42 best AI studies were analyzed to understand the RoB. Using the AP(ai)Bias paradigm, the top 19 studies were then chosen using the raw-cutoff of 1.9. This was obtained using the intersection of the cumulative plot of "mean score vs. study" and score distribution. Finally, these studies were benchmarked against ROBINS-I and PROBAST paradigm. Our observation showed that AP(ai)Bias, ROBINS-I, and PROBAST had only 32%, 16%, and 26% studies, respectively in low-moderate RoB (cutoff>2.5), however none of them met the RoB hypothesis. Further, the aiSR analysis recommends six primary and six secondary recommendations for the non-randomized AI for ARDS. The primary recommendations for improvement in AI-based ARDS design inclusive of (i) comorbidity, (ii) inter-and intra-observer variability studies, (iii) large data size, (iv) clinical validation, (v) granularity of COVID-19 risk, and (vi) cross-modality scientific validation. The AI is an important component for diagnosis of ARDS and the recommendations must be followed to lower the RoB.

A Concept Analysis on the Use of Artificial Intelligence in Nursing

Artificial intelligence (AI) has a considerable present and future influence on healthcare. Nurses, representing the largest proportion of healthcare workers, are set to immensely benefit from this technology. However, the overall adoption of new technologies by nurses is quite slow, and the use of AI in nursing is considered to be in its infancy. The current literature on AI in nursing lacks conceptual clarity and consensus, which is affecting clinical practice, research activities, and theory development. Therefore, to set the foundations for nursing AI knowledge development, the purpose of this concept analysis is to clarify the conceptual components of AI in nursing and to determine its conceptual maturity. A concept analysis following Morse's approach was conducted, which examined definitions, characteristics, preconditions, outcomes, and boundaries on the state of AI in nursing. A total of 18 quantitative, qualitative, mixed-methods, and reviews related to AI in nursing were retrieved from the CINAHL and EMBASE databases using a Boolean search. Presently, the concept of AI in nursing is immature. The characteristics and preconditions of the use of AI in nursing are mixed between and within each other. The preconditions and outcomes on the use of AI in nursing are diverse and indiscriminately reported. As for boundaries, they can be more distinguished between robots, sensors, and clinical decision support systems, but these lines can become more blurred in the future. As of 2021, the use of AI in nursing holds much promise for the profession, but conceptual and theoretical issues remain.

Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approach

Marked enhancement of the fibroglandular tissue on contrast-enhanced breast magnetic resonance imaging (MRI) may affect lesion detection and classification and is suggested to be associated with higher risk of developing breast cancer. The background parenchymal enhancement (BPE) is qualitatively classified according to the BI-RADS atlas into the categories "minimal," "mild," "moderate," and "marked." The purpose of this study was to train a deep convolutional neural network (dCNN) for standardized and automatic classification of BPE categories.This IRB-approved retrospective study included 11,769 single MR images from 149 patients. The MR images were derived from the subtraction between the first post-contrast volume and the native T1-weighted images. A hierarchic approach was implemented relying on 2 dCNN models for detection of MR-slices imaging breast tissue and for BPE classification, respectively. Data annotation was performed by 2 board-certified radiologists. The consensus of the 2 radiologists was chosen as reference for BPE classification. The clinical performances of the single readers and of the dCNN were statistically compared using the quadratic Cohen's kappa.Slices depicting the breast were classified with training, validation, and real-world (test) accuracies of 98%, 96%, and 97%, respectively. Over the 4 classes, the BPE classification was reached with mean accuracies of 74% for training, 75% for the validation, and 75% for the real word dataset. As compared to the reference, the inter-reader reliabilities for the radiologists were 0.780 (reader 1) and 0.679 (reader 2). On the other hand, the reliability for the dCNN model was 0.815.Automatic classification of BPE can be performed with high accuracy and support the standardization of tissue classification in MRI.

AI in Health: State of the Art, Challenges, and Future Directions

INTRODUCTION

Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health.

OBJECTIVE

The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future.

METHODS

Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed.

RESULTS

The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed.

CONCLUSION

Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.

Privacy in the age of medical big data

Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence in particular has promised to revolutionize medical practice from the allocation of resources to the diagnosis of complex diseases. But with big data comes big risks and challenges, among them significant questions about patient privacy. Here, we outline the legal and ethical challenges big data brings to patient privacy. We discuss, among other topics, how best to conceive of health privacy; the importance of equity, consent, and patient governance in data collection; discrimination in data uses; and how to handle data breaches. We close by sketching possible ways forward for the regulatory system.

Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion

STUDY DESIGN

A cross-sectional database study.

OBJECTIVE

The aim of this study was to train and validate machine learning models to identify risk factors for complications following posterior lumbar spine fusion.

SUMMARY OF BACKGROUND DATA

Machine learning models such as artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex datasets. ANNs have yet to be used for risk factor analysis in orthopedic surgery.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent posterior lumbar spine fusion. This query returned 22,629 patients, 70% of whom were used to train our models, and 30% were used to evaluate the models. The predictive variables used included sex, age, ethnicity, diabetes, smoking, steroid use, coagulopathy, functional status, American Society for Anesthesiology (ASA) class ≥3, body mass index (BMI), pulmonary comorbidities, and cardiac comorbidities. The models were used to predict cardiac complications, wound complications, venous thromboembolism (VTE), and mortality. Using ASA class as a benchmark for prediction, area under receiver operating curves (AUC) was used to determine the accuracy of our machine learning models.

RESULTS

On the basis of AUC values, ANN and LR both outperformed ASA class for predicting all four types of complications. ANN was the most accurate for predicting cardiac complications, and LR was most accurate for predicting wound complications, VTE, and mortality, though ANN and LR had comparable AUC values for predicting all types of complications. ANN had greater sensitivity than LR for detecting wound complications and mortality.

CONCLUSION

Machine learning in the form of logistic regression and ANNs were more accurate than benchmark ASA scores for identifying risk factors of developing complications following posterior lumbar spine fusion, suggesting they are potentially great tools for risk factor analysis in spine surgery.

LEVEL OF EVIDENCE

3.

A prospective pilot study investigating the musculoskeletal pain in postmenopausal breast cancer patients receiving aromatase inhibitor therapy

BACKGROUND

Although arthralgia is a known adverse effect of aromatase inhibitor (ai) treatment in postmenopausal breast cancer patients, few studies have carried out a comprehensive evaluation of the nature, onset, and incidence of musculoskeletal (msk) pain in these patients. We therefore used a pilot study to identify conditions or markers predictive of pain.

METHODS

For 24 weeks, we monitored 30 eligible postmenopausal women starting ai therapy. Pre-existing and incident msk conditions and pain were assessed clinically and with ultrasonography of the hands and wrists. In addition, patient questionnaires were used to assess pain before and during ai therapy. Biochemical markers were measured at baseline and at regular intervals after anastrozole therapy began. Gene profiling studies were carried out before and 48 hours after the initial ai administration.

RESULTS

Over the 24-week study period, 20 participants (67%) showed no pain symptoms; 5 (17%) experienced low or moderate pain at baseline, which did not increase with ai treatment; and during therapy, 5 (17%) showed exacerbation of pain attributable to osteoarthritis of the hand and to finger flexor tenosynovitis. Although all 30 participants had some degree of msk conditions before anastrozole therapy started, the pre-existing conditions did not necessarily predispose the women to increased pain during anastrozole treatment. Higher levels of urinary N-telopeptides of type i collagen were associated with the groups presenting pain, suggesting a higher extent of pre-existing bone resorption, without significant evolution over the 24-week treatment period. Slightly higher levels of 1,25(OH)(2) vitamin D(3) were observed at baseline in patients with pain increase, but did not significantly change during treatment; however, average levels of 25(OH) vitamin D(3) increased, likely because of supplementation. Although biochemical markers did not discriminate efficiently between pain groups, a signature of 166 genes in peripheral blood mononuclear cells was identified that could stratify patients into the various groups observed in this pilot study. The gene signature was enriched in components of inflammatory signalling and chemokine expression, of antitumoural immunity pathways, and of metabolic response to hormones and xenobiotics, although no clinically significant association could be made in the present study, considering the small number of patients. Nevertheless, the observed trend suggests the feasibility of developing surrogate predictive markers of msk pain. Patient compliance was high in this study and was not affected by pain exacerbation.

CONCLUSIONS

Baseline msk assessment showed pre-existing causes for pain in most of the study patients before initiation of the ai. Exacerbation of existing osteoarthritis pain and tenosynovial symptoms was the primary cause of pain increase. Musculoskeletal pain assessment at baseline and prompt treatment of pain symptoms may help to optimize adherence to ai therapy. The value of routinely assessing inflammatory markers such as C-reactive protein and erythrocyte sedimentation rate was not supported by our pilot study. Gene expression profiles in peripheral blood mononuclear cells may be further explored in larger-scale studies as stratification markers to identify patients at risk of developing arthralgia.

Allelic imbalance (AI) identifies novel tissue-specific cis-regulatory variation for human UGT2B15

Allelic imbalance (AI) is a powerful tool to identify cis-regulatory variation for gene expression. UGT2B15 is an important enzyme involved in the metabolism of multiple endobiotics and xenobiotics. In this study, we measured the relative expression of two alleles at this gene by using SNP rs1902023:G>T. An excess of the G over the T allele was consistently observed in liver (P<0.001), but not in breast (P=0.06) samples, suggesting that SNPs in strong linkage disequilibrium with G253T regulate UGT2B15 expression in liver. Seven such SNPs were identified by resequencing the promoter and exon 1, which define two distinct haplotypes. Reporter gene assays confirmed that one haplotype displayed approximately 20% higher promoter activity compared to the other major haplotype in liver HepG2 (P<0.001), but not in breast MCF-7 (P=0.540) cells. Reporter gene assays with additional constructs pointed to rs34010522:G>T and rs35513228:C>T as the cis-regulatory variants; both SNPs were also evaluated in LNCaP and Caco-2 cells. By ChIP, we showed that the transcription factor Nrf2 binds to the region spanning rs34010522:G>T in all four cell lines. Our results provide a good example for how AI can be used to identify cis-regulatory variation and gain insights into the tissue specific regulation of gene expression.

Prostate cancer aggressiveness locus on chromosome 7q32-q33 identified by linkage and allelic imbalance studies

The biologic aggressiveness of prostate tumors is an important indicator of prognosis. Chromosome 7q32-q33 was recently reported to show linkage to more aggressive prostate cancer, based on Gleason score, in a large sibling pair study. We report confirmation and narrowing of the linked region using finer-scale genotyping. We also report a high frequency of allelic imbalance (AI) defined within this locus in a series of 48 primary prostate tumors from men unselected for family history or disease status. The highest frequency of AI was observed with adjacent markers D7S2531 (52%) and D7S1804 (36%). These two markers delineated a common region of AI, with 24 tumors exhibiting interstitial AI involving one or both markers. The 1.1-Mb candidate region contains relatively few transcripts. Additionally, we observed positive associations between interstitial AI at D7S1804 and early age at diagnosis (P=.03) as well as a high combined Gleason score and tumor stage (P=.06). Interstitial AI at D7S2531 was associated with a positive family history of prostate cancer (P=.05). These data imply that we have localized a prostate cancer tumor aggressiveness loci to chromosome 7q32-q33 that is involved in familial and nonfamilial forms of prostate cancer.

Prognostic role of p27Kip1 and apoptosis in human breast cancer

Human breast carcinoma is biologically heterogeneous, and its clinical course may vary from an indolent slowly progressive one to a course associated with rapid progression and metastatic spread. It is important to establish prognostic factors which will define subgroups of patients with low vs high risk of recurrence so as to better define the need for additional therapy. Additional characterization of the molecular make-up of breast cancer phenotypes should provide important insights into the biology of breast cancer. In the present study, we investigated apoptosis, expression of p27Kip1 and p53 retrospectively in 181 human breast cancer specimens. In addition, their relevance to the biological behaviour of breast cancer was examined. Our studies found a significant association among high histological grade, high p53, low apoptosis and low p27. Our results also demonstrated that, in human breast cancer, low levels of p27 and apoptotic index (AI) strongly correlated with the presence of lymph node metastasis and decreased patient survival. In node-negative patients, however, p27 also had prognostic value for relapse-free and overall survival in multivariate analysis. Furthermore p27 and AI had predictive value for the benefits of chemotherapy. These latter observations should prompt prospective randomized studies designed to investigate the predictive role of p27 and AI in determining who should receive chemotherapy in node-negative patients.

Clinical advantage of valsartan

Valsartan is a specific angiotensin II receptor antagonist with high selectivity for the AT(1) receptor subtype. After oral administration of single or repeated once-daily doses, valsartan 40-80 mg inhibits the pressor response to angiotensin II for 24 hours. In patients with mild-to-moderate hypertension, efficacy of valsartan appears to be independent of age, sex, and race, and is at least equivalent to that of calcium antagonists, ACE inhibitors, or thiazide diuretics. Response rate to valsartan 160 mg o.d. is significantly greater than after receiving losartan 100 mg o.d. Valsartan has additive effects with other antihypertensive drugs and combination therapy is effective in severe hypertension and in hypertension with renal insufficiency, where renal function is well maintained. Valsartan has good tolerability with a side-effect profile indistinguishable from placebo and superior to that of comparable drugs. Valsartan does not cause cough or adverse metabolic effects; first dose hypotension and rebound hypertension on abrupt withdrawal have not been encountered. Valsartan has clear clinical advantage in the management of hypertension. Its impact on prognosis in patients with a high risk of cardiovascular morbidity and mortality is under evaluation.

The structure of spatial receptive fields of neurons in primary auditory cortex of the cat

Transient broad-band stimuli that mimic in their spectrum and time waveform sounds arriving from a speaker in free space were delivered to the tympanic membranes of barbiturized cats via sealed and calibrated earphones. The full array of such signals constitutes a virtual acoustic space (VAS). The extra-cellular response to a single stimulus at each VAS direction, consisting of one or a few precisely time-locked spikes, was recorded from neurons in primary auditory cortex. Effective sound directions form a virtual space receptive field (VSRF). Near threshold, most VSRFs were confined to one quadrant of acoustic space and were located on or near the acoustic axis. Generally, VSRFs expanded monotonically with increases in stimulus intensity, with some occupying essentially all of the acoustic space. The VSRF was not homogeneous with respect to spike timing or firing strength. Typically, onset latency varied by as much as 4-5 msec across the VSRF. A substantial proportion of recorded cells exhibited a gradient of first-spike latency within the VSRF. Shortest latencies occupied a core of the VSRF, on or near the acoustic axis, with longer latency being represented progressively at directions more distant from the core. Remaining cells had VSRFs that exhibited no such gradient. The distribution of firing probability was mapped in those experiments in which multiple trials were carried out at each direction. For some cells there was a positive correlation between latency and firing probability.

Comparison of two semen extenders in terms of in vitro development of bovine embryos following IVF

In order to conform with current EC standards with regard to antibiotic cover, the Norwegian Cattle Association is currently investigating the use of Biladyl as an alternative to the milk-based extender which has been traditionally used in Norway. A study was carried out to investigate the effect of using semen frozen with either milk extender or Biladyl on the outcome of in vitro fertilization and embryo culture. Semen from 6 Norwegian Red bulls was used. There was a significant difference p < 0.05 in terms of cleavage rate between the 2 extenders for 1 bull, 78.2% vs 94.9% for milk and Biladyl extenders, respectively, and for the overall total of 71.3% vs 76.1% for milk and Biladyl extenders, respectively. There were no significant differences in terms of blastocyst yield amongst any of the bulls. In conclusion, the results suggest that Biladyl can be used as a replacement for the traditional milk-based extender without any adverse effects on blastocyst yields following in vitro fertilization.

Evaluation of spermatological parameters used to predict the fertility of frozen bull semen

Post-thaw motility, velocity and acrosome integrity of frozen semen were determined in 18 bulls with varying fertility (average non-return rates: 71.3 (+/- 2.8)--range: 65.2-75.7). Five semen straws were investigated from each bull. The average values for sperm motility (percentage motile spermatozoa), sperm velocity (graded from 0-3) and acrosome integrity (proportion of spermatozoa with intact acrosome) were 67.5%, 2.5 and 79.3%, respectively. Significant correlations were found between sperm motility and velocity, but not between sperm motility and acrosome integrity. Both sperm motility and velocity were significantly related to bull fertility. It was concluded that of the post-thaw semen characteristics investigated in this study these 2 parameters provided a reliable basis for prediction of bull fertility.

Assessment of the post partum reproductive performance of the Icelandic dairy cow during a 3 year period

The time of ovulation, artificial insemination (AI), conception and conception rate were studied in 412 Icelandic dairy cows from 2 different areas during a period of 3 years. To assess these parameters the progesterone level was measured in sequential samples of milk and the status of the genital organs evaluated by monthly rectal palpations. The time and number of AI was recorded and the conception rate calculated and compared with the other parameters. Furthermore, the length of the calving interval and gestation period were determined. The effects of year, season, area, age, herd and parity on these parameters were evaluated.

The overall mean for the time of first post partum ovulation was 42 days and varied from 29 to 49 days between herds. This is a considerably longer time than recorded for many other breeds of dairy cows. The effects of season, area and herd were significant for the time of first post partum ovulation.

The overall time of first post partum AI and conception were 74 days and 97 days, respectively. In spite of late onset of ovarian activity in Icelandic dairy cows, these two pararmeters are comparable to those of other breeds of cattle. The effects of season, parity and herd were significant for these two parameters.

The conception rate to first post partum AI was 59% and the number of AI per conception was 1.6. The time of conception correlated closely (r = 0.5) with the time of first post partum AI, but the conception rate to first AI increased and the number of AI per conception decreased with increasing time of first post partum AI.

The calving interval was 382 days and the gestation period was 287 days.

Sodium deficiency of grazing cattle in Papua New Guinea

Sodium deficiency was suspected from low saliva sodium concentrations in cattle at various sites in the lowlands of Papua New Guinea. In an experiment at Erap, Morobe Province crossbred and pedigree Brahman heifers supplemented with copper, cobalt and/or common salt showed no response to copper or cobalt supplementation. There was a significant growth response (P less than 0.01) to salt supplementation over a 16 week period confirming sodium deficiency in these animals. The response in the crossbreds was twice that in the purebreds. Supplemented crossbred animals grew 0.78 kg/day over the 16 week experimental period.