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Return to work (RTW) is a marker of functional recovery in cancer patients, with quality of life, financial and social implications. We investigated frequency and factors associated with RTW in a cohort of patients younger than 66 years, with newly diagnosed multiple myeloma (MM), uniformly treated with a bortezomib-based induction followed by autologous stem cell transplantation (ASCT). Socio-economic and working status data were collected by a self-administered questionnaire. One hundred and eighty-six patients entered the study. Of whom, 145 (78%) where employed at diagnosis, which was more frequent in younger (median 55 vs. 60 years, p < 0.001), men (59.3% vs. 34.2%, p = 0.004), and with college studies (44.8% vs. 24.4%, p = 0.008). Forty-three (30%) of the 145 patients who had a job at diagnosis, RTW after ASCT in a median of 5 (range 1-27) months. Factors independently associated with RTW were having three or more children (HR 2.87, 95% CI 1.33-6.18), college studies (HR 2.78, 95% CI 1.21-6.41), and a family income >40 × 10(3)€/year (HR 2.31, 95% CI 1.12-4.78). In conclusion, the frequency of RTW herein reported in MM patients seems lower than reported in other malignancies. The risk factors observed may guide the design RTW programs.

Purpose Information systems (IS) strategic alignment is a significant chief information officers (CIO) and top management issue because of its impact on a firm’s performance and profitability. Previous studies have primarily examined informal dimension’s influence on IS strategic alignment. Nevertheless, a few research works have emphasised cultural dimension’s effect. The purpose of this paper is to empirically investigate and bring out organisational culture’s influence on IS strategic alignment. Notably, it highlights the most significant culture types, according to the Competing Value Framework (Cameron et al., 2006). Design/methodology/approach Empirical setting comprises a quantitative approach using a survey based on a sample of 160 business managers (BMs) of 53 large companies located in Tunisia with international activities and being in the post-implementation operational use phase of their enterprise resource planning (ERP) systems. The partial least square (PLS) method has been used for data analysis. Findings The results provide an empirical evidence supporting a positive and significant organisational culture’s influence on the IS strategic alignment. The findings also show that “Clan Culture” (Internal/Flexibility-oriented culture) positively influences IS alignment along with the strategic priorities. These findings provide guidance and help understand how, through clan culture, the company can contribute significantly to the success of its ERP systems strategic alignment during the most critical phase, namely, post-implementation. Originality/value Despite abundant work related to IS alignment topic, little research, to the authors’ knowledge, has been undertaken in considering organisational culture’s influence. Thus, this research aims to fill this gap and to raise new questions about IS alignment. First, this study puts together organisational culture (through the Competing values Framework) and strategic alignment (through the IS use dimension) in a single research model to analyse four culture types’ direct effect on IS alignment. Second, this study is innovative in its use of the ERP post-implementation as an empirical framework. The post-implementation phase is often played down in research work in favour of the upstream pre-implementation phases. Furthermore, the findings bring together theoretical and practical insights on both IS-business strategic alignment and ERP post-implementation. Thus, future research could emphasise the role of clan culture in the efficiency of ERP systems strategic alignment during the usage phase. Building on these findings, BM, CIO and top management are advised to promote this culture type based on communication, information sharing and the spirit of internal partnership – so that their ERP systems are used appropriately and aligned with the company’s strategic priorities.

Multiple myeloma (MM) is a recurrent malignancy with a high impact on quality of life. Improved survival relies on the combination of drugs and extended duration of therapy, raising concerns on its toxicity burden in elderly patients. Health-related quality of life measurements attent to capture health aspects relevant to patients other than efficacy. This prospective study aimed to understand the relationship between MM-related symptomatology and other quality of life dimensions using the EORTC QLQ-MY20 questionnaire in individuals with relapsed or refractory MM. Irrespective of treatment modality, over 50% of patients who responded to treatment had significant omprovements of reported scores in all domains. Conversely, disease progression was associated with score deterioration not only in the MM-related symptoms domain but also in all other domains. HRQoL adds valuable information to the established efficacy endpoints but an adequate interpretation of HRQoL outcomes in randomized trials should require stratification according to response.

Alzheimer’s disease (AD) is the most leading symptom of neurodegenerative dementia; AD is defined now as one of the most costly chronic diseases. For that automatic diagnosis and control of Alzheimer’s disease may have a significant effect on society along with patient well-being. The Mini Mental State Examination (MMSE) is a prominent method for identifying whether a person might have dementia and about the dementia severity respectively. These methods are time-consuming and require well-educated personnel to administer.

Automatic diagnosis and monitoring of Alzheimer’s Disease (AD) can have a significant impact on society as well as the well-being of patients. It is known that Alzheimer’s disease (AD) influences the language abilities of affected peoples. Hence, the diagnosis of Alzheimer’s disease using speech-based features is gaining growing attention. The purpose of this article is extracting a set of acoustic features from Dementia Bank conversations from subjects with and without Alzheimer’s disease. Extracted features will be trained with Machine Learning (ML) algorithms to testing the detection accuracy.

The 2ZB-1.8 model planting barrier seeder was designed by combining the serious soil desertification and difficulties during desert controlling in northern farming-pastoral areas of China. The structure and dimension of rack were determined by combining the working width of machine and structure distribution of all parts. The statics analysis and optimization of rack were analyzed by applying finite element software ANSYS. The result shows that the stability, rigidity and strength will be better when dimension and material of rack are 100mm×80mm×5 mm and 45# square steel, respectively. The stress and strain of machine after loading could meet with the demand of working performance.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by the insidious onset of cognitive, emotional and language disorders. These attacks are sufficiently intense to affect the daily social and professional lives of patients. Today, in the absence of a reliable diagnosis and effective curative treatments, fighting this disease is becoming a real public health issue, prompting research to consider non-drug techniques. Among these techniques, speech processing is proving to be a relevant and innovative field of investigation. Several Machine Learning algorithms achieved promising results in distinguishing AD from healthy control subjects. Alternatively, many other factors such as feature extraction, the number of attributes for feature selection, used classifiers, may affect the prediction accuracy evaluation. To surmount these weaknesses, a model is suggested which include a feature extraction step followed by imperative attribute selection and classification is achieved using a machine learning classifiers. The current findings show that the proposed model can be strongly recommended for classifying Alzheimer's patient from healthy individuals with an accuracy of 79%.

More and more patients worldwide suffer from end-stagerenal disease which often is secondary to diabetes (e.g.36.9% of all US patients with end-stage renal disease havea diabetic nephropathy [1]). Indeed, both types of diabetesare burdened by long-term complications, including retin-opathy and nephropathy. Nephropathy occurs in 25–40%of type 1 and type 2 diabetic patients [2], and is character-izedbythepresenceofabnormalamountsofproteinsintheurine, a sign of alteration in the renal filtration capabilitiesof the nephron. In many patients, diabetic nephropathy(DN) progresses to end-stage renal disease. However, it iscurrently impossible to reliably predict which and whendiabetic patients will develop nephropathy and progressto kidney failure. The persistent presence of albumin inthe urine is considered predictive of the subsequent devel-opment and clinical progression of DN [3,4]. Currently,microalbuminuria (MA) is considered the first clinicalevidence of incipient diabetic nephropathy. It is definedas an urinary albumin excretion of 30–300 mg/day andcharacterizes the stage III of diabetic nephropathy accord-ing to Mogensen’s classification [5]. However, despite itscapacity to highlight nephron damage, MA is a non-spe-cific marker of DN especially in subjects with type 2 dia-betes. Indeed, only 30–45% of microalbuminuric type 2diabetic patients will develop overt proteinuria over morethan 10 years [6], and MA is also proposed as a markerof high cardiovascular risk in diabetic and non-diabeticpatients [7], as recently highlighted [8]. Thus, MA shouldnot be considered an early and specific marker of DN.Urineandplasmaproteomicshavegainedmuchattentionas tools for the identification of diagnostic and prognosticbiomarkers of renal diseases. For many years now, it hasbeen hoped that proteomic methods would unveil earlierandmorespecificbiomarkersthanMA[9].Someprevious-ly published reviews have focused on the methodologicalaspects of proteomics [10,11] or on the approaches for dis-coveringandvalidatingdiagnosticmarkers[12,13].Aspro-teomics studies devoted to DN biomarker discovery begantobepublishedin2004[14],inthisarticle,wewillcompre-hensively review the available information published sincethen.Targeting DN biomarkers: an overview of the experimentalmethodsTable 1 summarizes the key information available from theanalysis of the literature. Urine is the biological fluid thatattracted most of the interest in the proteomic quest of DNbiomarkers. This makes sense because the protein compos-ition of urine might correctly reflect the renal functionalabnormalities associated with DN [15]. Moreover, urinehas some unique advantages such as its availability in largequantities as well as the solubility and stability of its pro-tein content [16]. Nevertheless, serum from type 2 diabeticpatients (four studies) [17–20] and pooled haemofiltratesfrom patients suffering from chronic renal insufficiency(most of them were also diabetics) (one work) [21] werealso analysed.Concerning the choice of separation and identificationtechniques (Table 1), two-dimensional gel electrophoresis(2D-GE) was frequently employed (however in differentexperimental set-ups). This is likely due to the fact that2D-GE easily provides a semi-quantitative estimation ofthe amount of each separated protein (spot intensity) andsome qualitative information about the size and the post-translational modifications of such proteins. These positivefeatures compensate for the limitations of 2D-GE such aslow throughput and a possible lack of sensitivity. Profilingmethods were chosen for several studies (indicated inTable 1). They generate a list of mass peaks which needthen to be assigned to a specific peptidic/protein se-quence. Surface-enhanced laser desorption/ionizationtime-of-flight (SELDI-TOF) mass spectrometry (a prote-omic method that enables mass fingerprinting of proteinsretained onto an affinity chip) was used in four differentstudies [18,22–24]. Interestingly, in each work, a differentseparationsurface(ProteinChip)wasconsideredasthemost