Approximately 340,000 knee replacements are performed each year in the USA and UK. Around 1% of patients who have had knee replacement develop deep infection around the prosthesis: periprosthetic knee infection. Treatment often requires a combination of one or more major operations and antibiotic therapy. This study aimed to understand and characterise patients’ experiences of periprosthetic knee infection.Qualitative semi-structured interviews were conducted with 16 patients  who experienced periprosthetic knee infection and subsequent revision treatment in six National Health Service orthopaedic departments. Interviews were audio-recorded, transcribed, anonymised and analysed thematically. The concept of biographical disruption was used to frame our analysis, and four transcripts double-coded for rigour. Patients were interviewed between two and 10 months after surgical revision.Participant experiences can be characterised according to three aspects of biographical disruption which we have used to frame our analysis: onset and the problem of recognition; emerging disability and the problem of uncertainty, and chronic illness and the mobilisation of resources. Participants’ experiences of infection and treatment varied, but everyone who took part reported that infection and revision treatment had devastating effects on them. Participants described use of social and healthcare support and a need for more support. Some participants thought that the symptoms that they had first presented with had not been taken seriously enough.Periprosthetic knee infection and its treatment can be life-changing for patients, and there is a need for greater support throughout treatment and lengthy recovery. Future work could look at preparedness for adverse outcomes, help-seeking in impactful situations, and information for healthcare professionals about early signs and care for periprosthetic infection. In the USA and UK, approximately 340,000 primary knee replacements are performed each year , 2. In 2Periprosthetic infection is commonly treated with either one-stage or two-stage revision. One-stage revision treatment consists of one major surgery, during which the prosthesis and infected tissues are removed before a new prosthesis is re-implanted . Two-staPrevious research on infection after joint replacement highlights how infection and treatment can have profoundly negative impact on all aspects of patients’ lives –18. Due meaning as consequence: the problems experienced by people as a result of activity restriction, social disadvantage and their impact on daily life; and meaning as significance: the connotations that illnesses carry, in a cultural context. Critique of Bury’s work often focusses on these two distinct meanings and the degrees to which each is true, with the concept of ‘normal illness’ [So far, research has not drawn on social science theories to extend and deepen understanding of the impact of periprosthetic infection on patients. In understanding the impact of major health and treatment events, insights from the social science of health and illness are useful, particularly regarding disruption to people’s lives, and how people negotiate and manage major change brought about by illness. Bury’s work on ‘biographical disruption’ is widely recognised as making a major contribution to the greater understanding of experiences of health and illness events. It has been used in health research to highlight how illness disrupts people’s expected life course in multiple ways, changing the structures of daily life, challenging and redefining people’s sense of identity and constructed biography –22. Buryillness’  set agaiillness’ . Based oillness’ , periproIn this qualitative interview study, we explore the experience of periprosthetic knee infection and its impact on patients’ lives and draw on the concept of biographical disruption to sensitise us to the meanings of significance and consequences of this major health event for individuals in the context of their social worlds.The study was a qualitative, interview study comprising in-depth interviews and thematic analysis.Eligible participants were people aged 18 years and over, with periprosthetic knee infection and experience of one-stage or two-stage revision surgery. They were all patients who had received treatment at one of six participating UK National Health Service (NHS) orthopaedic departments in the 12 months before recruitment.Between January 2016 and September 2016, 33 patients were invited to participate. A research nurse at each centre reviewed outpatient clinic lists. All eligible patients were provided with information packs and asked to complete and return a reply form to the research team indicating if they were interested in discussing participation. The research team then contacted those patients who expressed interest and arranged a mutually convenient visit to discuss the study and to conduct an interview if they agreed to participate. At that visit, patients had the opportunity to ask any further questions and were asked to provide their written, informed consent, including consent to audio-recording the interview and to publication of anonymised quotations. Once consent was provided, interviews took place at the same visit.Final sample size was intended to depend on the achievement of saturation, evidenced by no new themes arising from the data , 31. SatInterviews were conducted in participants’ homes, by one of the research team’s two experienced qualitative researchers (CM and AM). The interviewers were not previously known to the participants. Topic guides were developed in collaboration with the research unit’s patient and public involvement forum . Topic g, the researcher (CM) read and re-read the transcripts, inductively and deductively coded them and sorted coded data into themes. Deductive coding involved working “down” from pre-existing understandings from previous research, which sensitised us to the data [A team science approach was used to ensure robust analysis, whereby our research was collaboratively conducted by a small team of researchers . Intervithe data . A seconthe data . Bury dethe data . Within the data .Ethical approval was granted by NRES Committee South West - Exeter (14/SW/0072).The sample consisted of 9 patients who received one-stage revision treatment, and 7 patients who received two-stage revision treatment; 9 men and 7 women, aged 59–80 years (mean age 72 years) Table .Table 1SFor 14 patients, this was their first revision surgery. One had received a previous two-stage revision. With the exception of one participant whose primary knee replacement was the result of a fracture, participants’ primary replacements had been elective procedures to relieve pain associated with osteoarthritis.The results highlighted participants’ varied experiences, in particular regarding infection onset and subsequent treatment. Infection onset occurred over periods ranging from immediately to 19 years after their primary surgery. All participants felt that infection was life-changing, describing it as “devastating”, “traumatic” and causing a “considerably restricted life”. Patients’ narratives suggest that the experience of infection can be understood as a temporal situation, which can be mapped onto Bury’s three aspects of disruption: Onset and the problem of recognition; emerging disability and the problem of uncertainty; and chronic illness and the mobilisation of resources. Participants discussed the ways in which infection and its treatment impacted on, and disrupted the life course within each aspect.Our analysis suggests that the experience of infection and its treatment causes patients to negotiate major change brought about by this illness event, and to redefine their biographies. Participants described the disruption of taken-for-granted daily routines, uncertainty about the impact and course of infection and treatment, and anxiety and fear about the future. Illustrative quotations are referred to throughout the text and are presented in Tables The onset of infection marked a biographical shift from an expected normal course of recovery after knee replacement, to one which was abnormal. Participants described the onset of infection in terms of length of time after surgery, diverse sensations and impact. The onset of infection did not appear to follow a predictable path, and varied from “immediately” after primary replacement (participants felt it never got better) to 19 years afterwards. Although some participants recalled discussing the risk of infection before they had their primary replacement, most did not recollect such conversations. Those participants who could recollect a discussion reported being informed that along with many other factors, infection was a “risk” of joint replacement, however despite this, they still felt largely unprepared for infection.One participant reported having no indication that anything was wrong until she was suddenly unable to place her foot on the floor. Two patients felt so ill after their infection had manifested, they thought that they would die: one of these participants reported sudden onset at 19 years afterwards, the other described how she never reached her expected “Nirvana” of two years to fully recover after the primary replacement, and despite questioning clinicians, was eventually admitted to hospital by emergency ambulance after collapsing at home. Some participants described more obvious signs that something was wrong, such as red, warm and leaking wounds or scars; others described swelling or the presence of sinus tracts or lumps. Although some described severe pain, others did not.The process of recognising and reporting the illness to a healthcare professional was problematic for all participants, and routes to diagnosis varied. The length of time between first reporting that there was a problem, and treatment at one of the six orthopaedic departments, ranged from immediately to five years. Some healthcare professionals recognised that participants’ symptoms might indicate infection, and made a diagnosis quickly. For some participants diagnosis was prolonged as their symptoms were not immediately thought to be indicative of infection. Participants who experienced a slower route to diagnosis were dissatisfied and felt that their concerns had not been taken seriously enough by healthcare professionals. Although some patients were insistent with healthcare professionals that there was a problem, others felt frustrated with themselves for not “speaking up” when they had intuitively felt that something was wrong. Patients’ reasoning for not challenging healthcare professionals about their symptoms included viewing surgeons as expert and therefore trusting, or having “faith” in their abilities, not wanting to be perceived as “bolshie or rude or pedantic”, not knowing appropriate questions to ask, and fearing they would “hold everything up” by challenging decisions. Consequently, some participants felt that their pursuit of referral had been considerably delayed, and that if their infection had been diagnosed sooner, the impact on their life may have been less severe.Participants described conflicting reactions to their diagnoses. The diagnosis of infection was a shock to some participants. Despite a late diagnosis some participants reported feeling “pleased” and “relief” that treatment was imminent. Others felt “disappointed”, “unlucky” or “annoyed”. One patient described feeling vindicated, and described how he had felt the surgeon who conducted his primary knee replacement “blamed” him for a lack of improvement after surgery. At the point of diagnosis, some patients felt uncertain about how the infection might progress, and what the subsequent treatment might entail. Despite a firm diagnosis the cause of the infection was often unknown, and participants reported feeling shocked, and a mixture of fear and relief at the point of diagnosis, but also concerned and uncertain about their future.For the participants in this study, the onset of infection was not perceived as a “normal” part of their biography. It was a shock, and a source of uncertainty and anxiety, about its cause, the treatment and the prognosis. The onset and diagnosis of infection was traumatic for many participants, and something for which they were unprepared.Surgical and antibiotic treatment for infection profoundly disrupted participants’ everyday lives in multiple ways, reinforcing a biographical shift from a perceived normal to an altered situation and sense of self. Participants’ responses to antibiotic therapy varied. Not all patients felt unwell during antibiotic treatment and most did not experience any adverse effects. However, some patients experienced unpleasant and distressing side effects, including nausea, sickness, loss of appetite, diarrhoea and weight loss. Describing her low mood and distress after three weeks of unpleasant side-effects, one patient felt that she could no longer cope with her treatment regimen.Surgical treatment for infection impacted on patients’ physical mobility and function, and some participants experienced associated social, psychological and financial burdens. Participants lived with mobility and lifestyle limitations including being unable to drive, work, sleep, carry out domestic duties, or walk without pain. Leisure activities, social engagements and visits to family were often cancelled, and participants were unable to continue their previous lifestyle as a result of severe immobility. Infection also had a financial impact on participants. One participant cancelled her holiday, losing money on flights and accommodation; another felt “lucky” that he had completed all of his mortgage payments in the same month as his operation, believing that he would have been forced to sell his house otherwise as he could no longer work. This profound physical, social, psychological and financial disruption led to participants describing their lives as “on hold”.Participants spoke of the trust and faith they placed in their surgeon. Some patients retained a trust in their surgeon especially if the same surgeon that performed their primary knee replacement also performed the revision operation. However, others lost trust in the surgeon who conducted their primary replacement. One participant described how she did not “trust” her original surgeon to perform a revision operation, because she felt that her insistence that something was wrong had not been taken seriously enough. Additionally, she described an initial conversation with her surgeon in which she was assured she would be able to walk, and engage in sporting activity after knee replacement: activities she was unable to do. In contrast, one participant insisted the same surgeon who had performed his primary replacement also performed his subsequent three revisions.Participants expressed a sense of uncertainty about the eradication or the return of infection. Many patients reported that a recurrence of infection was their main concern for the future: some patients sought post-operative assurances from their surgeons about the “percentage” or “likelihood” of eradicating infection completely; other patients described their hopes that the infection would not return. This sense of uncertainty led to patients feeling “terrified” and “very anxious” of the impact that a recurrent infection and further surgery would have on their lives.The significance of participants’ treatment was neither downplayed, nor thought to be a “normal” part of their biography. For the participants in this study, the treatment of infection was a complex, and often lengthy process. In line with this, the consequences of participants’ treatment for infection were severe for all participants, with profound physical, social, psychological and financial disruption, which profoundly changed their daily lives. Participants described withdrawal from their valued activities and relationships as a result of treatment, which prevented them from doing things they had previously enjoyed.Participants’ responses to the disruption of infection and treatment involved a restructuring of their personal and social involvements. Participants experienced a sense of hopelessness, and felt that their personal identity had changed over the course of the infection and treatment: from a dependent, capable self to an uncertain, increasingly dependent self for whom the infection “takes over”. Participants found that taken-for-granted activities they were once easily able to undertake were no longer possible. Most participants felt that their relationships with family and friends had been disrupted, either because of mobility restrictions, tiredness, or embarrassment in social situations. One participant described how he felt he placed an unfair burden on his wife who missed out on social and leisure opportunities. Another participant described how she felt she was a burden to her older children and had tried to avoid socialising with them.In terms of mobilising resources to face a new and unexpected situation, participants relied mostly on support from social networks including family, friends and neighbours in relation to cooking meals, shopping for food, undertaking domestic duties, and accessing their own home. The presence of a supportive social network was fundamental to all participants’ recovery, and participants described how maintaining their social network was of great concern.Participants described how they made adaptations to their physical home environment, in order to manage their recovery after revision treatment. Some moved out of their home to live with others during their recovery. One patient moved in with her daughter, during her post-operative recovery, but became anxious when left alone in the house due to her immobility. As self-care became difficult, participants’ spouses or children helped them to maintain their personal hygiene. Participants also discussed being unable to shower, or creating complex routines to enable them to do so, including travelling to a family member’s home to use a walk-in shower.In terms of medical support and resources, some participants reported needing more care and support with their post-operative and long-term recovery from Primary Care doctors and allied health professionals, in the period after surgical revision treatment. One participant felt that the lack of psychological support after her discharge from hospital had led to her low moods. Another patient described an absence of support from both clinicians and her spouse led to her being unable to manage her medication once discharged. Post-operative physiotherapy varied between treatment centres. Some participants reported having minimal physiotherapy input, or none at all, whilst others reported satisfactory post-operative physiotherapy. However, patients’ experiences varied. Only one patient received a course of hydrotherapy. Another described having only one physiotherapy appointment in her eight-day recovery in hospital, at which she was shown how to use cold therapy ice pads, but still felt uncertain of how to use them.There was a stark contrast between participants’ expectations of primary knee replacement, and the reality of life after periprosthetic infection. Many participants described their altered life course in ways that inferred an acceptance of their situation as a new normal, rather than an illness event. Participants discussed feelings of uncertainty, about the recurrence of infection, or requiring lifelong antibiotic medication. When asked about their original preconceptions of primary knee replacement, participants largely spoke of reduced pain, walking without discomfort, and a return to being “fit and active”. In contrast to their preconceptions, one patient had experienced a “superb” recovery after his primary replacement until his infection four years later, however at the time of interview was unable to walk without discomfort, and described a painful and stiff joint: he was fearful that his infection had not cleared. One patient had experienced a “terrible” and lengthy recovery after her primary replacement, and despite feeling satisfied with the revision operation, felt that she had lost two years of her life.The frustration of coping with either pain or immobility in their daily lives contributed to patients’ low mood. The impact of both infection and treatment on wider family and significant relationships was profound, with some participants acknowledging a fear of dependency. Participants’ responses to the infection and treatment also expressed a sense of fragility, using phrases such as “I’m hopeless now”, “I do need answers now because I can’t carry on”, and “It makes me feel useless.”Participants’ narratives suggest that both the meanings as significance and consequence of symptoms and treatment for periprosthetic infection create a biographically abnormal and profoundly disruptive experience for patients, wherein social identities are challenged and redefined.People diagnosed with periprosthetic infection faced an altered and unexpected situation, in which they experienced profound disruption to their life course according to Bury’s theory . InfectiParticipants made sense of their experiences of infection through the narratives that they shared, giving meaning to the events of infection that disrupted the structures of their everyday lives, and the foundations that underpin these structures . NarratiPeriprosthetic infection can be life-changing, both physically and psychologically, and placing pressure on patients’ social and supportive relationships. The impact on participants’ mobility and physical function restricted their ability to participate in social roles and events as they had previously, subsequently affecting their sense of personal identity. Some participants were forced to adapt their home environments or move home entirely to cope with their recovery. Participants described disruption to their personal identity and relationships with others, in a context where infection and treatment precluded their former positive experiences and meanings , 22. WitDespite its widespread application, biographical disruption has been debated , 25. YetThis study provides new information about patients’ experiences of periprosthetic knee infection. Saturation was achieved in the sample of 16 patients from six UK NHS orthopaedic centres, and we took care to ensure rigour in analysis through a double coding and team science approach. We acknowledge that it is possible that the inclusion of more patients from additional study centres may have elicited supplementary findings. We also acknowledge that a study limitation, common to all research which employs opt-in consent is that there is an inherent self-selection bias, however, achievement of saturation gives us confidence that the sampling was appropriate in quantity and breadth. The study was conducted in the UK context, but we suggest that the experience of infection after knee replacement resonates with other contexts, including social impact and need for care. Interviewing participants between two and 10 months after revision surgery may have introduced recall bias but this approach gave the study the opportunity to explore the longer-term impact of infection, treatment and recovery.The impact of periprosthetic infection is wide-ranging, and research to date has not paid sufficient attention to the experiences of people who have this complication after knee replacement. Participants within this study described a disruption to their everyday behaviours, a loss of identity, growing dependency on families and social support networks, as well as uncertainty about their futures. As such we suggest that periprosthetic knee infection may be described as an assault on patients’ physical self, sense of self and life course. Further research into the impact of infection might take a longitudinal, prospective approach to explore recovery and change over time in more detail. Our findings lead us to suggest that clinicians in primary, secondary and community care should be supported to provide consistency in care, not only through conveying the importance and urgency of infection diagnosis, but also by being vigilant to the early warning signs and symptoms of periprosthetic infection. Indeed, early diagnosis of periprosthetic infection maximises the chance of prosthesis retention . Future


There is still no effective approach to overcome the problem of credit evaluation for Chinese students. In absence of a reliable credit evaluation system for students, the university students have to only apply through online peer-to-peer (P2P) loan platforms because Chinese financial institutions typically reject students' loan applications. Lack of students' financial records hinders financial institutes and banks to routinely evaluate the students' credit status and assign loans to them. Hence, this paper attempted to benefit from university students' diversified daily behavior data, and logistic regression (LR) and gradient boosting decision tree (GBDT) algorithms were also used to develop robust credit evaluation models for university students, in which the validation of the proposed models was assessed by a real-time P2P lending platform. In this study, the students' overdue behavior in returning books to university library was used as an index. With training 17838 samples, the proposed models performed well, while GBDT-based model outperformed in identification of “bad borrowers.” Based on the proposed models, a self-sponsored peer-to-peer loan platform was established and developed in a Chinese university for ten months, and the achieved findings demonstrated that adopting such credit evaluation models can effectively reduce the default ratio. The higher education system in China has been rapidly developed since 1990s. In 2017, the number of Chinese students who have enrolled in higher education system has reached 46.10 million. There is a broad business development space in the student credit market in the global scale. Unlike students in Western countries, Chinese students are typically forbidden to reach credit cards through Chinese banks until they are graduated and could be able to find a proper job . High inIt is well known that data mining is the process of discovering patterns in large datasets. Chinese students typically live in universities' dormitories, which is less common among students in Western countries. A Chinese student can easily purchase different items, access the Internet, and eat in various canteens using his/her campus card. The campus card can also be an entry card to enter sports clubs, library, and dormitory as well. Swiping cards produce plenty of records, including consumption record, in- and out-record, etc. These patterns and consumption data are typically collected and stored by student administration offices of a university. In this paper, it was attempted to utilize such data to develop a credit evaluation system. These datasets vary in formats and are remarkably different from financial transactions, which are routinely used by classical credit evaluation methods.This paper develops a credit evaluation model for Chinese students using the multisource dataset provided by an anonymous Chinese university, and the developed method is validated by a real-time peer-to-peer (P2P) lending platform.In recent years, data mining techniques have been used in credit scoring systems. Dave Girouard, the CEO of Upstart company, believes that there are loopholes in the current credit system. Upstart is an American P2P credit agency launched in May, 2014, and has facilitated on approving 8700 loans, totally $12.50 million in 2014. This company believes that traditional credit score systems cannot properly describe young clients' repayment condition, thus they employed a robust big data method to assess students' level of education, background, and work experience . ZestFinThere are several hybrid systems that combine conventional algorithms together to improve classification ability. For example, to estimate the influence of the state of economy on loan losses, a linear regression method was combined with SVM, and this two-stage hybrid approach outperformed other techniques on prediction . A two-sAdditionally, to experimentally validate the developed credit model, the authors have implemented a P2P lending platform offering loans to students. Architecture of P2P reflects that users can directly connect with each other in order to share their information or lending-based issues . In suchThe rest of the paper is organized as follows. This research is based on a dataset provided by an anonymous Chinese university; after feature extraction, the data are transformed and loaded into the data warehouse. The dataset includes 78716461 items of 31586 students who have enrolled during the years of 2013 and 2015 in two campuses. Besides, it involves several aspects: basic information, library loan records, records of entrance to the library and dormitory, grades, consumption records, and scholarship records. All data were kept confidential to protect students' privacy, and the students were empowered with their own online data.Z-score method, and the Z vectors can be obtained using the following equation:Si denotes the standard deviation of the ith feature. The detailed information of all features after data preprocessing is listed in Real-time datasets are susceptible to various quality issues, such as missing values, different data structures, data redundancy, and imbalanced data . Herein,In different application fields, the definition of a “bad customer” accordingly varies. Generally speaking, for a credit risk management, a “bad customer” typically implies a customer with high possibility of default . In bankIn this paper, the university students were assessed in absence of their financial credit information. Regarding all behaviors happened in the university, behavioral data were collected by university's information center, and data were then processed by the system. It was revealed that there are numerous similarities between borrowing a book from library of university with loan borrowing or utilizing a credit card. To scrutinize the process of borrowing a book from a library, it is important to note that a book must be returned by the due time or the due date, and an overdue book will incur fines. In the university, a student is permitted to borrow only 10 books for 30 days, and if a student cannot return those books on time, he/she will be fined 0.5 CNY per each day and each book. To prevent overdue, a student can apply online only for one time to postpone the deadline for 15 days, while the request can be only submitted once per each book. Thus, if a student could successfully register his/her deferral request online, he/she will be allowed to take out a book for 45 days without any penalty. When the penalty reaches 5 CNY, a student will be prohibited to borrow books for 3 months. Similar to a bank or financial institution that regularly sends reminder messages to a debtor who has received a loan, the library of a university also has a reminding system to inform the students to return the books on time through automatically forwarding daily e-mails since 7 days before the deadline. Therefore, a “bad borrower” here is defined as an individual who has the highest frequency of overdue in terms of returning books which were borrowed from a library.Lib_borrow_exceed_cnt (denoting the frequency of returning overdue books), in which each rank involved the same number of students. A variable called label was also defined to indicate whether a student is a “good borrower,” who is denoted by 0 or a “bad borrower,” who is denoted by “1.” In this study, it was attempted to consider the students in the top group (with the highest frequency of returning overdue books) as “bad borrowers” (who are denoted by “1”), and the remaining include 0-default students as “good borrowers.” Students with no record of borrowing books were removed from dataset.Herein, the example on returning books to a library is taken as a symbol of borrowing behavior into consideration, and an index of “bad borrower” is defined that is widely used in credit evaluation in banking system. There is a common method in banking system to decide whether a customer's loan/credit card application is passed or rejected. For this purpose, first of all, all customers' credibility should be ranked by the forecasted probability of default which is given based on banks' credit models. Then, top 5% of customers with the highest default probability are considered as “bad customers,” and their loan/credit card applications are accordingly rejected. Similarly, in this study, it was attempted to pick out students who had default records of returning books, and they were divided into twenty groups and were ranked by a variable named Prob_Back, Prob_Nation, Lib_borrow_total_cnt, avg_grade, Prob_Sex, excellent_rate, Lib_borrow_avg_exceed_cnt, Lib_renew_prob, Lib_borrow_avg_exceed_time, fail_rate, avg_cost, sum_transaction_amount, mean_recharge. These features are explained in Before modeling, it was attempted to refine features in order to eliminate their dependencies. Herein, Pearson's correlation coefficient was used to refine features. After refinement, 13 features were selected as follows: To develop credit evaluation models, all the processed data were categorized into “training set” and “test set.” After preprocessing, 29741 students remained, including 487 “bad borrowers” and 29254 “good borrowers.” In addition, 288 “bad borrowers” and 17550 “good borrowers” were randomly assigned to training set, and the test set involved 240 “bad borrowers” and 14625 “good borrowers.” Eventually, 41 “bad borrowers” and 2434 “good borrowers” were assigned to both training set and test set. Owing to the skewness of dataset, it may cause overfitting of models. Thus, for both sets, it was attempted to randomly pick out 200 “bad borrowers” as well as 3800 “good borrowers” from their datasets at every turn, and the process was iterated for 20 times, until 20 training groups and 20 test groups would be reached. Here, after developing credit evaluation models, the students were generally divided into “bad borrowers” vs. “good borrowers” based on each individual's default probability, and the outputs showed that the proportion can be eventually reached at 5 : 195. It could effectively reduce the overfitting on nondefault set and cover the entire dataset as far as possible by means of the mentioned method.It is necessary to investigate the efficiency of the credit evaluation model. Percentage of correctly classified instances (including accuracy (ACC), true positive (TP) rate, true negative (TN) rate, false positive (FP) rate, and false negative (FN) rate) and receiver operating characteristic (ROC) curve are two conventional evaluation metrics. Accuracy calculates the ratio of correctly classified cases to the total number of cases , which iBesides ACC, the sensitivity (or the true positive rate) which measures the proportion of actual positives that are correctly detected and specificity which measures the proportion of actual negatives that are correctly detected are also studied. In this study, sensitivity is defined as the percentage of correctly predicted “bad borrowers” among true “bad borrowers.” The sensitivity is expressed asThe specificity (or the true negative rate) is defined as the percentage of correctly predicted “good borrowers” among true “good borrowers.” The specificity is given byAs an example, we examine a demo to show the necessities to adopt these two criteria. Consider the following two misclassification tables as shown in Tables specificity) [In addition, the ROC curve which was proposed by Hanley and McNeal in 1982 was herein adopted as well. The ROC curve is created by plotting the TP rate versus the FP rate at various threshold settings . The TP ificity) . Area unificity) . In thisT, including n students, e.g., X=, and each student has a feature vector involving j descriptions, e.g., Xk=, , xk ∈ R. Thus, the particular form of logistic regression model isLogistic regression is one of the most widely used techniques in statistical analysis, and it has possessed fewer classification errors in credit risk assessment . Assume π(x) logistic function is known as logit transformation:The transformation of the Then, those 13 features were forwarded into LR model, and the stepwise regression method was used to do further refinement. After that, 5 features that were not found significant were eliminated. The maximum likelihood estimates of the features remained can be found in p denotes the probability of being a “bad borrower” ranging from 0 to 1.After training by the training set (involving 288 “bad borrowers” and 17550 “good borrowers”), the LR model can be formulated asp, and the top 5% of students were predicted as “bad borrowers,” while others were predicted as “good borrowers.”Afterwards, all the students were ranked by S={xk, yk}k=1N, where xk ∈ Rm is input data and corresponding output is denoted by yk. The main goal is to find out an approximation F(x) that could minimize the expected value of a particular loss function L) [S, meaning that each sample has the same selection opportunity at the first step. Generating T-decision tree classifiers for the model requires T rounds of trained decision tree with T different training sample groups St. In round t, the function to determine the weight of sample k is denoted by Dr(k). In each round, after the construction of classifier Mt which provides a function Ft to map x to {−1, 1}, the value of Dr(k) is adjusted in terms of classification pattern by classifier Mt, and the training sample group St+1 is then generated in terms of Dt on S with sample replacement.As an ensemble learning method, GBDT is a successful algorithm and is applied to classify the numerical problems, that constructs a composite classifier by sequentially training differentiated classifiers in the gradient direction for reduction of the residual error, while that remarkably emphasizes on certain patterns . For they, F(x)) . InitialHere, the prediction performance of the two models is discussed. In the present study, a test dataset was used comprising of 14864 samples (with 14625 “good borrowers” and 240 “bad ones”). As mentioned above, to elude the skewness of data, 20 test groups were extracted, in which each group contained 200 “bad borrowers” and 3800 “good borrowers.” Tables To further assess continuous predictive performance of these two models, ROC curves were plotted. We selected one test group and ranked all the samples in 20 groups in descending order (each group contained 200 samples) by the default rate generated by LR-based model. After that, the cumulative ratio of true “bad borrowers” and “good borrowers” was calculated. The same process was undertaken for GBDT-based model on the same test group. Tables So far, we cannot confirm that the default records of borrowing books can denote poor personal credit especially on financial issues. If it did not make sense, the credit evaluation models we have built would be meaningless. Therefore, a P2P lending platform was developed based on our proposed credit evaluation model. The platform was implemented within the campus LAN  and can only be accessed by university students.To get a loan from the platform, a student must register with his/her student ID and phone number and agree with our terms of service. Behind the scenes, every registered user's credit score was calculated automatically. Considering that the nature of this loan service was for experiments, loans provided by the platform were all free of interest. The period of a loan was 30 days, and the user would get a bonus point if he/she repaid from the 14th day to the 30th day. The bonus point was designed to prevent those who did not have actual demanding for loan, showing up as lending and repaying in one single day or a quite short period. There was no explanation about the function of bonus point, but it turned out effective. There were only 31 out of 258 payments occurred from the first day to the 13th day. As soon as a user repaid on time (in 30 days), there would be no interest or service fee; otherwise, he/she would have to pay service fee which was 1% of the principal. There was a reminder system which can automatically send messages to remind debtors to repay, and it worked in the last 3 days of every loan period. In addition, if a student did not repay on time, we would give him/her a call from the 33rd day after his/her loan day until the day he/she repaid. The funds of this platform were raised from anonymous students in the same university.∗  (1 − p)+50), where p was the output of LR model which meant the probability of default. According to the rules of credit model, users whose credit scores were the lowest 5% should be regarded as “bad borrowers,” which meant that a student whose credit score was below 60 should be rejected to get a loan. However, as a contrast experiment, loans were also approved to students with credit scores below 60.We adopted LR-based model's results to calculate credit scores. Although both credit evaluation models had strong capability and were totally interchangeable, the reason why we chose LR-based model instead of GBDT was that the parsimony and computation speed of LR model are better. The credit score was equal to  was much higher than the number of postgraduate students (which was 67), and there were only two doctoral students. Freshmen and sophomores accounted for more than half of total debtors, which turned out that freshmen and sophomores had less disposable money to meet their daily consumption or other expenses so that they had to turn to lending networks. In comparison, juniors and seniors may have more ways to earn pocket money, so relatively, they did not have large demand for loans. In addition, almost all debtors were science and engineering students (account for 94%). However, given the fact that this anonymous university is a prestigious university of science and engineering, we cannot conclude convincingly that science and engineering students have more demand for loans than students of art.According to the rules of our credit model, students whose credit score were below 60 were “bad borrowers.” In this experiment, there were 13 predicted “bad borrowers.” The experiment is successfully completed based on a small-ranged P2P lending platform. However, there are still several limitations. First, this experiment is based on a condition that users do not have to pay any interest. Thus, it is not enough to infer what these students would behave if there was an interest rate. Secondly, the university in which the experiment is conducted is one of the top universities in China, and it may not be able to represent the general situation of all Chinese universities. Last, the scale of samples is too small. In order to examine the accuracy of our opinion, the range of this P2P lending platform should be developed in the future.This study demonstrates that data mining meshed with machine learning algorithms, such as LR and GBDT, can perform well for the purpose of students' credit evaluation who have enrolled in a Chinese university. It was herein to creatively adopt the default of returning books as the index of financial default. Besides, two credit evaluation models were developed based on LR and GBDT algorithms separately. By comparing the achieved results, both models have shown excellent performance, while the GBDT-based model possessed higher capability on recognizing “bad borrower.” The result of P2P lending platform showed that employing a robust credit evaluation model is helpful to screen loan applications and can significantly prevent occurrence of default cases. While university students had a good credit in average, rejection of applicants with low credit scores was efficient to reduce the default ratio from 3.5% to 1.2%. To set up a robust credit evaluation system among different universities, several issues need to be solved. For instance, most of the Chinese universities do not have data collection or information storage systems. Data analysis procedure is based on large amounts of raw data; thus, it is of great importance to establish unified data collection systems. In addition, regarding the heterogeneity of different universities, the feature extraction process should be more holistic. Also, universities should enhance their relationships with social organizations not only to further develop the credit evaluation system but also to motivate their students to use credit services.This study can be potentially generalized to other types of population. As for community finance, the problem of depicting the features and preferences of community population's expenditure behavior remained unsolved. Similar to university students, people in the same community have strong homogeneity. Only if the characters would be grabbed, product design and commercial promotion of local business can be more effective. Also, the proposed prediction methods can be applied to other university students' problems. Our team recently found a solution to foresee students' psychological problems by analyzing their reading information. It turns out the preference on book types has a strong association with one's psychology condition. Further research studies should be carried out on the issues associated with university students in the future.


Hypoglycaemia remains the most common metabolic adverse effect of insulin and sulfonylurea therapy in diabetes. Repeated exposure to hypoglycaemia leads to a change in the symptom complex that characterises hypoglycaemia, culminating in a clinical phenomenon referred to as impaired awareness of hypoglycaemia (IAH). IAH effects approximately 20–25% of people with type 1 diabetes and increases the risk of severe hypoglycaemia. This review focuses on the mechanisms that are responsible for the much higher frequency of hypoglycaemia in people with diabetes compared with those without, and subsequently how repeated exposure to hypoglycaemia leads to the development of IAH. The mechanisms that result in IAH development are incompletely understood and likely to reflect changes in multiple aspects of the counterregulatory response to hypoglycaemia, from adaptations within glucose and non-glucose-sensing cells to changes in the integrative networks that govern glucose homeostasis. Finally, we propose that the general process that incorporates many of these changes and results in IAH following recurrent hypoglycaemia is a form of adaptive memory called ‘habituation’.The online version of this article (10.1007/s00125-018-4548-8) contains a slideset of the figures for download, which is available to authorised users. The characteristic symptom complex that alerts an individual to hypoglycaemia is well described and is broadly represented by three distinct symptom clusters, namely ‘autonomic’ , ‘neuroglycopaenic’  or ‘general malaise’  . It is aIn this review and the accompanying reviews by Iqbal and Heller  and ChouAlthough hypoglycaemia can occur in people without diabetes (e.g. ‘reactive hypoglycaemia’), it is not common and, with the exception of hypoglycaemia occurring during severe sepsis or malnutrition, it is not usually severe or of potential pathological consequence. As a fuel, glucose is so fundamental to the survival of an organism that multiple systems are in play to ensure that a continuous supply of glucose is provided to the tissues of the body. These systems act in concert to ensure that glucose utilisation by the brain, liver, muscle and adipose tissue (white and brown), and glucose production and release into the blood stream by the liver and kidney, are tightly regulated. It seems highly likely that these systems do not exist in isolation but form parts of a highly integrated network designed both to monitor overall blood nutrient levels as well as fuel stores, and to direct fuels where and when needed to specific tissues. For instance, a liver–brain axis that is responsive to liver glycogen content was recently demonstrated to modulate the counterregulatory response (CRR) to insulin-induced hypoglycaemia . ReadersIn type 1 diabetes , hypoglycaemia is, by contrast, relatively common. There are three principal abnormalities that underlie this propensity to hypoglycaemia: (1) failure to clear circulating insulin during hypoglycaemia; (2) loss of normal pancreatic alpha cell responses; and (3) lower glucose threshold for release of counterregulatory hormones. Generally, the first two defects explain why low glucose levels develop more frequently in people with type 1 and type 2 diabetes in comparison with people without diabetes, while the third major defect (discussed in more detail in the next section) explains why a subset of people with type 1 and type 2 diabetes may be even more prone to hypoglycaemia and will occasionally suffer severe hypoglycaemia.The CRR is initiated when glucose levels fall beneath the normal range and is designed to restore normal glucose homeostasis. It encompasses hormonal, symptomatic and behavioural responses. In people without diabetes, the first response to a decline in blood glucose is a reduction in insulin secretion that begins while plasma glucose concentration is still in the physiological range (~4.4 mmol/l) . In contThe second major CRR defect is loss of physiological glucagon secretion, which is profound in type 1 diabetes  but also1c from 81 ± 12 mmol/mol (9.6 ± 1.1%) to 54 ± 8 mmol/mol  for release of counterregulatory hormone and symptom responses to experimentally-induced hypoglycaemia, as well as reduced magnitude of these responses . This islycaemia . ImportaA series of human clamp studies in the 1990s demonstrated that prior hypoglycaemia in individuals with  or withoWhile recurrent hypoglycaemia is recognised as the primary risk factor leading to the development of IAH, the underlying mechanism(s) responsible for this remain less well understood. There is an extensive literature that has examined this question and Fig. The initial focus of investigators was to determine if recurrent hypoglycaemia resulted in an increase in glucose and/or alternate fuel transport, or fuel storage. Certainly, studies in rodent models and humans show an increase in the capacity to transport glucose or alternate fuels through the blood–brain barrier and into neurons and astrocytes , 28, as ATP) channels and shift thresholds for glucose-sensing neuron activation. Certainly, recurrent hypoglycaemia increases the activity of neuronal glucokinase [ATP channel. Based on prior extensive animal research [Alternatively, it is possible that prior hypoglycaemia increases the capacity of the cell (neuron) to metabolise glucose, generating more ATP for any given glucose level, which would directly modulate ATP-sensitive potassium , the excitatory neurotransmitter glutamate  and the n humans . This of2-adrenergic or serotonergic signalling may play roles in the development of IAH [ATP channel activity, or through modulation of neurotransmitter release. They are secreted either in an autocrine fashion from neurons, released by neighbouring astrocytes or secreted from peripheral tissues, such as muscle. This knowledge has been derived from laboratory-based research, which has enabled proof-of-concept clinical trials. For instance, the inhaled highly-selective β2-adrenoreceptor agonist formoterol fumerate was shown to amplify the CRR to hypoglycaemia in a small group of participants with type 1 diabetes [More recently, there has been significant interest in discovering how cytokine, purine, steroid, opiate, βt of IAH , 38–42. diabetes . It is idiabetes . This radiabetes , as discIn a seminal review paper in the 1960s, Thompson and Spencer sought to describe the properties of a form of learning called ‘habituation’. Habituation is defined as a reduction in the psychological, behavioural or physiological response to a stimulus as a result of repeated or prolonged exposure. Habituation is a form of adaptive learning and is usually seen in the context of innate behaviours, presenting as a reduced response to repeated external or internal stimuli , 46.Habituation is now recognised to occur in virtually all organisms in response to a variety of stressful stimuli . At a biA further defining feature of a habituated response is that it should be possible to acutely restore the response through introduction of a novel, usually strong stimulus. This is a process referred to as ‘dishabituation’. In a recent study from the authors’ laboratory, introduction of a dishabituatory stimulus was indeed shown to restore normal CRRs to hypoglycaemia in a rodent model . In thisHypoglycaemia remains a concern for people with type 1 and type 2 diabetes. In diabetes, hypoglycaemia results from profound disturbances in glucose homeostasis and, in particular, the inability to ‘shut off’ exogenous insulin delivery and loss of normal physiological regulation of glucagon secretion. Recurrent hypoglycaemia leads to further marked suppression of counterregulatory hormonal and symptomatic responses to subsequent hypoglycaemia, a clinical phenomenon referred to as IAH. The mechanism(s) underlying the development of IAH are incompletely understood but may, or are perhaps likely, to involve multiple rather than single steps in the generation of a CRR. These adaptations occur on multiple levels, from within individual glucose and non-glucose-sensing cells, all the way up to the integrative networks that govern glucose homeostasis. Conceptually, IAH may result from habituation  to recurrent hypoglycaemia. This form of adaptation to repeated hypoglycaemia is aimed at inducing greater cellular tolerance of subsequent hypoglycaemia, but in fact increases susceptibility to future hypoglycaemia.By increasing our understanding of the mechanisms that lead to the development of IAH, we are better placed to consider how to approach the management of this condition. For instance, to resolve the problem of insulin dissipation, we are now aware that more physiological- and glucose-responsive insulin therapies are required. Restoration of physiological glucagon secretion would also be a major advance, but we must first obtain a clearer idea of why this defect develops in diabetes. Finally, habituation is best treated by avoidance of the habituating stimulus, namely hypoglycaemia, and this is currently best achieved through structured education, as discussed by Iqbal and Heller . FurtherESM(PPTX 486 kb)


Communal eating, whether in feasts or everyday meals with family or friends, is a human universal, yet it has attracted surprisingly little evolutionary attention. I use data from a UK national stratified survey to test the hypothesis that eating with others provides both social and individual benefits. I show that those who eat socially more often feel happier and are more satisfied with life, are more trusting of others, are more engaged with their local communities, and have more friends they can depend on for support. Evening meals that result in respondents feeling closer to those with whom they eat involve more people, more laughter and reminiscing, as well as alcohol. A path analysis suggests that the causal direction runs from eating together to bondedness rather than the other way around. I suggest that social eating may have evolved as a mechanism for facilitating social bonding. Feasting, the gathering together in groups for a communal meal, has long been of interest to both archaeologists Hayden  and anthWhile special occasions of this kind inevitably attract attention, in fact feasting on this scale represents the tip of an iceberg of communal eating that mainly focusses on family and friends. Family meals are widespread and commonplace in all cultures, and inviting friends or visitors to dine remains a regular social activity in most societies – with communal eating with guests being widely regarded as both the height of hospitality and an important way of getting to know people. Even in times when fast food dominates everyday culture, sitting down to eat with family and friends continues to be seen as important and desirable.Why do we do this? There is no intrinsic reason (other than the convenience of bulk cooking) that makes communal eating essential, especially for hunter-gatherers. Given that social meals inevitably take longer than eating alone, what is it about communal eating that is so beneficial? Potential benefits can be identified at three different levels: communal, networking and personal. These can be identified, respectively, with (a) building wider community and inter-community relationships, usually on a large scale but at infrequent intervals , (b) making and reinforcing (i.e. servicing) friendship and family relationships, usually on a modest scale and at more frequent (perhaps even daily) intervals and (c) at the personal level in terms of health benefits. The first two relate to indirect fitness benefits that accrue through the formation of mutual alliances at different levels (between versus within community), while the third relates to direct fitness in terms of health benefits that arise from well-formed social relationships.Friendships provide important health benefits, although the significance of these has only recently been appreciated. There is now considerable evidence, for example, to suggest that the size and quality of one’s social network has very significant consequences for one’s health, susceptibility to illness (and even death), wellbeing and happiness , a national stratified UK sample was commissioned through the polling agency OnePoll. A panel sample of 2000 adults aged over 18 years, balanced for regional distribution, age and gender, was sampled in one week in April 2016. In addition to demographic information , respondents were asked to rate how many meals they ate alone during the week and  how often they had eaten meals with different members of their extended network; they were also asked to rate, on 10-point analogue scales, how satisfied they felt with life, how worthwhile they felt their life to be, how happy they had been the day before, and  how much closer they felt to the people concerned afterwards. In respect of the evening meal, they were also asked to say how many people were present  and, on a simple binary choice, whether or not any of the following had occurred during the meal: laughter, reminiscences, jokes, singing, dancing, party games, drinking alcohol or eating chocolate. Laughter, singing, dancing and storytellling are all known to trigger the endorphin system  did have meals with family and friends at least sometimes Fig. . Even so1914 = 10.14, p < 0.0001), and were skewed towards pairs (including the respondent). Only 21% of lunch groups contained more than four individuals, and only 27% of dinner groups did so. Nonetheless, the average size of lunch and dinner groups . The two sexes did not differ on any of these indices .In terms of life satisfaction, 69% felt satisfied with their life, 67% had been happy on the day before and 70% felt that what they did in their life was worthwhile. However, only 46% of people trusted the people they met, and only 30% felt engaged with their local community. These indices of life satisfaction all correlated highly with each other , although in practice the difference  is small. Figure On average, respondents said they had 4.63 ± 3.06 people they could count on for emotional and other forms of support and help Fig. . As founp < 0.001).Figure 6,1991 = 43.993, p < 0.0001), with life satisfaction, engagement with the community and support clique size as the only significant factors .Asked if sharing a meal was a good way to bring people closer together, 76.4% said it was (with 18.1% not sure). When meeting someone new, women exhibited a greater preference for a lunchtime meeting (41% against 20% for an evening event), whereas men had a slight preference for the evening (25% vs 30% respectively). In contrast, 34% of people felt that an evening meal would be the better occasion to meet up with an old friend or family member, with 30% opting for a lunch event. Although women still preferred a lunchtime event (34%), a much higher proportion of them (32%) opted for an evening event in this case. Only 4% of people said they wouldn’t suggest a meal as a way to meet. A matched pairs t-test suggests that respondents felt significantly closer to a fellow diner after an evening meal than they did after a midday meal . However, only four of the variables had significant independent effects: number of diners, laughter, reminiscences, and the consumption of alcohol (Table p = 0.045). Notice that the frequency of jokes was not of itself important; rather, even though jokes might be used because they trigger laughter, it seems to be the laughter itself, whatever it is triggered by, that creates the sense of bonding. Similarly, the consumption of chocolate  had no detectable effect.It remains unclear whether it is eating together per se or something else that happens during the meal that creates the sense of social engagement. A definitive answer to this question would need an experimental design that was not possible in a survey study. Nonetheless, we can gain some insights by considering some of the activities that occur during social meals. Respondents were asked to rate how much closer they felt to the people with whom they had had their most recent evening meal , how many people had been present (four categories) and (as binary presence/absence variables) whether laughter, reminiscences, jokes, singing, dancing, party games, alcohol drunk or chocolate eaten had occurred. The frequencies with which each of these occurred are indicated in Table These survey data allow us to conclude (1) that people who eat socially are more likely to feel better about themselves and to have a wider social network capable of providing social and emotional support, (2) that eating with someone in the evening makes one feel closer to them than eating with them at midday and (3) that evening meals at which laughter and reminiscences occur and alcohol is drunk are especially likely to enhance feelings of closeness. Neither age nor sex had especially significant or noteworthy effects in any of these respects, other than to confirm the fact that, as noted in many previous studies, women typically have more close friends than men do. Intriguingly, the analyses suggest that feeling engaged with the local community is dimensionally orthogonal (i.e. unrelated) to feelings of personal happiness and satisfaction with one’s circumstances.in the hope that it will increase one’s feeling of closeness, but whether or not one actually feels closer afterwards is likely to depend on the meal itself and not on the expectation. In fact, the most convincing evidence for causal direction is provided by the path analysis of Fig. Causality is always difficult to determine from correlational data. One possibility is obviously that people who eat regularly with others have a wider circle of close friends as a result and thus feel more embedded in their communities. However, we cannot exclude the alternative possibility that having a wider circle of friends or being embedded in the community for other reasons (such as being an extravert) causes individuals to eat socially more often. Nonetheless, in their more qualitative responses, a significant proportion of respondents typically felt that having a meal together was an important way of making or reinforcing friendships, suggesting that this may be why they ate with others. Indeed, the fact that having eaten together increased respondents’ sense of closeness to the person concerned Table  would beExactly what it is about eating together that causes these effects isn’t completely clear, though the analysis in Table One potentially important finding is that evening meals are regarded as significantly more appropriate or valuable for building friendships than eating at midday. Casual observation suggests that many of our most important social activities happen in the evening; doing these things at night seems to have an added ‘magic’. Yet almost no one seems to have commented on this. In a recent analysis of conversational topics among San hunter-gatherers, Wiessner  noted thOver the past decade or so, considerable evidence has emerged that the number and quality of close friendships has a significant and direct impact on health, wellbeing and even survival (see, among a great many others, Waxler-Morrison et al.